1123 lines
40 KiB
Markdown
1123 lines
40 KiB
Markdown
# Holographic Display Implementation: True Holographic Rendering
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## Overview
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This document provides detailed implementation guidance for holographic display technology, focusing on true holographic rendering that leverages every available terrestrial, satellite, and auxiliary channel for seamless integration.
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## 1. Holographic Display Technology
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### 1.1 Light Field Display Implementation
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```python
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import torch
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import torch.nn as nn
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import numpy as np
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from typing import Dict, List, Optional, Tuple
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from dataclasses import dataclass
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import cv2
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@dataclass
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class LightFieldConfig:
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resolution: Tuple[int, int] = (1920, 1080)
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num_views: int = 64
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depth_layers: int = 32
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wavelength: float = 550e-9 # 550nm green light
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pixel_pitch: float = 6.4e-6 # 6.4μm pixel pitch
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viewing_distance: float = 0.5 # 50cm viewing distance
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class LightFieldDisplay:
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def __init__(self, config: LightFieldConfig):
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self.config = config
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self.light_field_generator = LightFieldGenerator(config)
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self.view_interpolator = ViewInterpolator(config)
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self.depth_renderer = DepthRenderer(config)
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self.display_controller = DisplayController(config)
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async def render_light_field(self, scene_data: SceneData) -> LightFieldData:
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"""Render light field for holographic display"""
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# Task: Implement light field rendering
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# - Multi-view rendering
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# - Depth-based rendering
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# - Real-time light field generation
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# - View-dependent rendering
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# Generate light field
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light_field = await self.light_field_generator.generate_light_field(scene_data)
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# Interpolate views
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interpolated_views = await self.view_interpolator.interpolate_views(light_field)
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# Render depth layers
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depth_layers = await self.depth_renderer.render_depth_layers(scene_data)
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# Combine for display
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display_data = await self.combine_for_display(interpolated_views, depth_layers)
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return display_data
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async def combine_for_display(self, views: torch.Tensor, depth_layers: torch.Tensor) -> LightFieldData:
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"""Combine views and depth layers for display"""
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# Implementation for display combination
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# - View synthesis
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# - Depth integration
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# - Light field reconstruction
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# - Display optimization
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# Synthesize views
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synthesized_views = await self.synthesize_views(views, depth_layers)
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# Reconstruct light field
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light_field = await self.reconstruct_light_field(synthesized_views)
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# Optimize for display
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display_optimized = await self.optimize_for_display(light_field)
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return LightFieldData(display_optimized)
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class LightFieldGenerator:
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def __init__(self, config: LightFieldConfig):
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self.config = config
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self.camera_array = CameraArray(config)
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self.ray_tracer = RayTracer(config)
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self.light_transport = LightTransport(config)
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async def generate_light_field(self, scene_data: SceneData) -> torch.Tensor:
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"""Generate light field from scene data"""
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# Implementation for light field generation
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# - Multi-view capture simulation
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# - Ray tracing
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# - Light transport
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# - View synthesis
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# Simulate camera array
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camera_views = await self.camera_array.capture_views(scene_data)
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# Ray trace for each view
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ray_traced_views = []
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for view in camera_views:
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ray_traced = await self.ray_tracer.trace_rays(view, scene_data)
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ray_traced_views.append(ray_traced)
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# Apply light transport
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light_field = await self.light_transport.apply_transport(ray_traced_views)
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return torch.stack(light_field)
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class CameraArray:
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def __init__(self, config: LightFieldConfig):
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self.config = config
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self.camera_positions = self.generate_camera_positions()
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self.camera_orientations = self.generate_camera_orientations()
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def generate_camera_positions(self) -> torch.Tensor:
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"""Generate camera array positions"""
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# Implementation for camera array generation
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# - Grid layout
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# - Spacing calculation
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# - Position optimization
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# - Coverage analysis
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# Generate grid positions
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x_positions = torch.linspace(-0.1, 0.1, int(np.sqrt(self.config.num_views)))
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y_positions = torch.linspace(-0.1, 0.1, int(np.sqrt(self.config.num_views)))
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# Create meshgrid
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X, Y = torch.meshgrid(x_positions, y_positions)
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# Flatten and add depth
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positions = torch.stack([X.flatten(), Y.flatten(),
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torch.zeros(self.config.num_views)], dim=1)
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return positions
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async def capture_views(self, scene_data: SceneData) -> List[torch.Tensor]:
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"""Capture views from camera array"""
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# Implementation for view capture
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# - Perspective projection
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# - View transformation
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# - Image rendering
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# - Quality optimization
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views = []
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for i, position in enumerate(self.camera_positions):
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# Set camera position
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camera_matrix = self.create_camera_matrix(position, self.camera_orientations[i])
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# Render view
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view = await self.render_view(scene_data, camera_matrix)
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views.append(view)
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return views
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def create_camera_matrix(self, position: torch.Tensor, orientation: torch.Tensor) -> torch.Tensor:
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"""Create camera transformation matrix"""
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# Implementation for camera matrix creation
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# - Translation matrix
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# - Rotation matrix
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# - Projection matrix
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# - View matrix
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# Translation
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translation = torch.eye(4)
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translation[:3, 3] = position
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# Rotation
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rotation = torch.eye(4)
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rotation[:3, :3] = orientation
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# Combine
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camera_matrix = torch.matmul(translation, rotation)
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return camera_matrix
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class RayTracer:
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def __init__(self, config: LightFieldConfig):
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self.config = config
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self.ray_generator = RayGenerator(config)
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self.intersection_tester = IntersectionTester(config)
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self.shader = Shader(config)
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async def trace_rays(self, view_data: torch.Tensor, scene_data: SceneData) -> torch.Tensor:
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"""Trace rays for view rendering"""
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# Implementation for ray tracing
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# - Ray generation
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# - Intersection testing
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# - Shading computation
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# - Color accumulation
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# Generate rays
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rays = await self.ray_generator.generate_rays(view_data)
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# Test intersections
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intersections = await self.intersection_tester.test_intersections(rays, scene_data)
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# Apply shading
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shaded_result = await self.shader.apply_shading(intersections, scene_data)
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return shaded_result
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async def generate_rays(self, view_data: torch.Tensor) -> torch.Tensor:
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"""Generate rays for ray tracing"""
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# Implementation for ray generation
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# - Primary rays
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# - Secondary rays
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# - Ray direction calculation
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# - Ray origin setup
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height, width = view_data.shape[:2]
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# Generate pixel coordinates
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y_coords, x_coords = torch.meshgrid(
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torch.arange(height), torch.arange(width)
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)
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# Convert to normalized device coordinates
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ndc_x = (x_coords.float() / width) * 2 - 1
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ndc_y = (y_coords.float() / height) * 2 - 1
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# Create ray directions
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ray_directions = torch.stack([ndc_x, ndc_y, torch.ones_like(ndc_x)], dim=-1)
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return ray_directions
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```
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### 1.2 Volumetric Display Implementation
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```python
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class VolumetricDisplay:
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def __init__(self, config: VolumetricConfig):
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self.config = config
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self.voxel_renderer = VoxelRenderer(config)
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self.volume_reconstructor = VolumeReconstructor(config)
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self.interactive_controller = InteractiveController(config)
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self.collaboration_manager = CollaborationManager(config)
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async def render_volumetric(self, volume_data: VolumeData) -> VolumetricResult:
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"""Render volumetric display"""
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# Task: Implement volumetric display
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# - 3D voxel rendering
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# - Real-time volume reconstruction
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# - Interactive 3D manipulation
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# - Multi-user collaboration
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# Render voxels
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voxel_rendering = await self.voxel_renderer.render_voxels(volume_data)
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# Reconstruct volume
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reconstructed_volume = await self.volume_reconstructor.reconstruct_volume(volume_data)
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# Apply interactive controls
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interactive_result = await self.interactive_controller.apply_interaction(
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reconstructed_volume
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)
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# Handle collaboration
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collaborative_result = await self.collaboration_manager.handle_collaboration(
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interactive_result
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)
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return collaborative_result
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class VoxelRenderer:
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def __init__(self, config: VolumetricConfig):
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self.config = config
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self.ray_marcher = RayMarcher(config)
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self.volume_sampler = VolumeSampler(config)
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self.transfer_function = TransferFunction(config)
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async def render_voxels(self, volume_data: VolumeData) -> torch.Tensor:
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"""Render 3D voxels"""
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# Implementation for voxel rendering
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# - Ray marching
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# - Volume sampling
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# - Transfer function application
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# - Color composition
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# Ray march through volume
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ray_march_result = await self.ray_marcher.march_rays(volume_data)
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# Sample volume
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sampled_volume = await self.volume_sampler.sample_volume(volume_data)
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# Apply transfer function
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colored_volume = await self.transfer_function.apply_transfer_function(sampled_volume)
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# Compose final rendering
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final_rendering = await self.compose_rendering(ray_march_result, colored_volume)
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return final_rendering
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async def march_rays(self, volume_data: VolumeData) -> torch.Tensor:
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"""Ray march through volume"""
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# Implementation for ray marching
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# - Ray generation
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# - Step size calculation
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# - Sampling along rays
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# - Early termination
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# Generate rays
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rays = self.generate_rays()
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# Initialize ray marching
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ray_positions = rays.origins
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ray_directions = rays.directions
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# March rays
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accumulated_color = torch.zeros_like(ray_positions)
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accumulated_alpha = torch.zeros(ray_positions.shape[:3])
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for step in range(self.config.max_steps):
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# Sample volume at current positions
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samples = await self.sample_volume_at_positions(ray_positions, volume_data)
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# Apply transfer function
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colors, alphas = await self.transfer_function.apply(samples)
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# Accumulate colors and alphas
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accumulated_color = accumulated_color + colors * (1 - accumulated_alpha.unsqueeze(-1))
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accumulated_alpha = accumulated_alpha + alphas * (1 - accumulated_alpha)
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# Update ray positions
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ray_positions = ray_positions + ray_directions * self.config.step_size
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# Early termination
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if torch.all(accumulated_alpha > 0.99):
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break
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return accumulated_color
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class VolumeReconstructor:
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def __init__(self, config: VolumetricConfig):
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self.config = config
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self.reconstruction_algorithm = ReconstructionAlgorithm(config)
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self.noise_reducer = NoiseReducer(config)
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self.artifact_remover = ArtifactRemover(config)
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async def reconstruct_volume(self, volume_data: VolumeData) -> torch.Tensor:
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"""Reconstruct volume from sparse data"""
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# Implementation for volume reconstruction
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# - Sparse reconstruction
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# - Noise reduction
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# - Artifact removal
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# - Quality enhancement
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# Apply reconstruction algorithm
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reconstructed = await self.reconstruction_algorithm.reconstruct(volume_data)
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# Reduce noise
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denoised = await self.noise_reducer.reduce_noise(reconstructed)
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# Remove artifacts
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cleaned = await self.artifact_remover.remove_artifacts(denoised)
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return cleaned
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async def reconstruct(self, volume_data: VolumeData) -> torch.Tensor:
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"""Apply reconstruction algorithm"""
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# Implementation for reconstruction
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# - Compressed sensing
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# - Dictionary learning
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# - Sparsity constraints
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# - Optimization
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# Initialize reconstruction
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reconstructed = torch.zeros_like(volume_data.sparse_data)
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# Apply compressed sensing
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for iteration in range(self.config.max_iterations):
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# Forward projection
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projection = await self.forward_project(reconstructed)
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# Compute residual
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residual = volume_data.sparse_data - projection
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# Backward projection
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update = await self.backward_project(residual)
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# Apply sparsity constraint
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reconstructed = await self.apply_sparsity_constraint(reconstructed + update)
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return reconstructed
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@dataclass
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class VolumetricConfig:
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resolution: Tuple[int, int, int] = (256, 256, 256)
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max_steps: int = 1000
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step_size: float = 0.01
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transfer_function_resolution: int = 256
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max_iterations: int = 100
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sparsity_weight: float = 0.1
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```
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## 2. Holographic Rendering Pipeline
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### 2.1 Geometry Processing
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```python
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class HolographicRenderingPipeline:
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def __init__(self, config: HolographicConfig):
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self.config = config
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self.geometry_processor = GeometryProcessor(config)
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self.lighting_calculator = LightingCalculator(config)
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self.hologram_generator = HologramGenerator(config)
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self.display_output = DisplayOutput(config)
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async def render_hologram(self, scene_data: SceneData) -> HologramResult:
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"""Render hologram through complete pipeline"""
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# Task: Implement holographic rendering pipeline
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# - Geometry processing
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# - Lighting calculation
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# - Hologram generation
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# - Display output
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# Process geometry
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processed_geometry = await self.geometry_processor.process_geometry(scene_data)
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# Calculate lighting
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lighting_result = await self.lighting_calculator.calculate_lighting(
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processed_geometry, scene_data
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)
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# Generate hologram
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hologram = await self.hologram_generator.generate_hologram(lighting_result)
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# Prepare for display
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display_data = await self.display_output.prepare_display(hologram)
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return HologramResult(display_data)
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class GeometryProcessor:
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def __init__(self, config: HolographicConfig):
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self.config = config
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self.mesh_generator = MeshGenerator(config)
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self.lod_manager = LODManager(config)
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self.occlusion_culler = OcclusionCuller(config)
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self.spatial_optimizer = SpatialOptimizer(config)
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async def process_geometry(self, scene_data: SceneData) -> ProcessedGeometry:
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"""Process geometry for holographic rendering"""
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# Implementation for geometry processing
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# - Real-time mesh generation
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# - Level-of-detail management
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# - Occlusion culling
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# - Spatial optimization
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# Generate meshes
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meshes = await self.mesh_generator.generate_meshes(scene_data)
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# Apply LOD
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lod_meshes = await self.lod_manager.apply_lod(meshes)
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# Perform occlusion culling
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visible_meshes = await self.occlusion_culler.cull_occluded(lod_meshes)
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# Optimize spatial layout
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optimized_geometry = await self.spatial_optimizer.optimize_layout(visible_meshes)
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return optimized_geometry
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async def generate_meshes(self, scene_data: SceneData) -> List[Mesh]:
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"""Generate meshes from scene data"""
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# Implementation for mesh generation
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# - Point cloud processing
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# - Surface reconstruction
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# - Mesh optimization
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# - Quality assessment
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meshes = []
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for object_data in scene_data.objects:
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# Process point cloud
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processed_points = await self.process_point_cloud(object_data.points)
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# Reconstruct surface
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surface = await self.reconstruct_surface(processed_points)
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# Optimize mesh
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optimized_mesh = await self.optimize_mesh(surface)
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meshes.append(optimized_mesh)
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return meshes
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async def reconstruct_surface(self, points: torch.Tensor) -> Surface:
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"""Reconstruct surface from point cloud"""
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# Implementation for surface reconstruction
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# - Poisson reconstruction
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# - Marching cubes
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# - Surface smoothing
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# - Hole filling
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# Apply Poisson reconstruction
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poisson_surface = await self.apply_poisson_reconstruction(points)
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# Apply marching cubes
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mesh_surface = await self.apply_marching_cubes(poisson_surface)
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# Smooth surface
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smoothed_surface = await self.smooth_surface(mesh_surface)
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# Fill holes
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filled_surface = await self.fill_holes(smoothed_surface)
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return filled_surface
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class LightingCalculator:
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def __init__(self, config: HolographicConfig):
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self.config = config
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self.global_illuminator = GlobalIlluminator(config)
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self.ray_tracer = RealTimeRayTracer(config)
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self.dynamic_lighting = DynamicLighting(config)
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self.material_simulator = MaterialSimulator(config)
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async def calculate_lighting(self, geometry: ProcessedGeometry,
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scene_data: SceneData) -> LightingResult:
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"""Calculate advanced lighting for holographic rendering"""
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# Implementation for lighting calculation
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# - Global illumination
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# - Real-time ray tracing
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# - Dynamic lighting
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# - Material simulation
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# Calculate global illumination
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global_illumination = await self.global_illuminator.calculate_gi(geometry, scene_data)
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# Apply ray tracing
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ray_traced_lighting = await self.ray_tracer.trace_lighting(geometry, scene_data)
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# Apply dynamic lighting
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dynamic_lighting = await self.dynamic_lighting.apply_dynamic_lighting(
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geometry, scene_data
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)
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# Simulate materials
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material_lighting = await self.material_simulator.simulate_materials(
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geometry, scene_data
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)
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# Combine lighting results
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combined_lighting = await self.combine_lighting(
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global_illumination, ray_traced_lighting,
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dynamic_lighting, material_lighting
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)
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return combined_lighting
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async def calculate_gi(self, geometry: ProcessedGeometry, scene_data: SceneData) -> torch.Tensor:
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"""Calculate global illumination"""
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# Implementation for global illumination
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# - Light propagation
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# - Bounce calculation
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# - Indirect lighting
|
|
# - Ambient occlusion
|
|
|
|
# Initialize light propagation
|
|
light_propagation = await self.initialize_light_propagation(scene_data.lights)
|
|
|
|
# Calculate light bounces
|
|
for bounce in range(self.config.max_bounces):
|
|
# Propagate light
|
|
propagated_light = await self.propagate_light(light_propagation, geometry)
|
|
|
|
# Calculate indirect lighting
|
|
indirect_lighting = await self.calculate_indirect_lighting(propagated_light, geometry)
|
|
|
|
# Update light propagation
|
|
light_propagation = await self.update_light_propagation(
|
|
light_propagation, indirect_lighting
|
|
)
|
|
|
|
# Apply ambient occlusion
|
|
final_gi = await self.apply_ambient_occlusion(light_propagation, geometry)
|
|
|
|
return final_gi
|
|
```
|
|
|
|
### 2.2 Hologram Generation
|
|
|
|
```python
|
|
class HologramGenerator:
|
|
def __init__(self, config: HolographicConfig):
|
|
self.config = config
|
|
self.fresnel_kirchhoff = FresnelKirchhoffIntegrator(config)
|
|
self.quantum_corrector = QuantumCorrector(config)
|
|
self.interference_calculator = InterferenceCalculator(config)
|
|
self.phase_optimizer = PhaseOptimizer(config)
|
|
|
|
async def generate_hologram(self, lighting_result: LightingResult) -> torch.Tensor:
|
|
"""Generate hologram from lighting data"""
|
|
# Implementation for hologram generation
|
|
# - Fresnel-Kirchhoff integration
|
|
# - Quantum corrections
|
|
# - Interference calculation
|
|
# - Phase optimization
|
|
|
|
# Apply Fresnel-Kirchhoff integral
|
|
fresnel_result = await self.fresnel_kirchhoff.integrate(lighting_result)
|
|
|
|
# Apply quantum corrections
|
|
quantum_corrected = await self.quantum_corrector.apply_corrections(fresnel_result)
|
|
|
|
# Calculate interference patterns
|
|
interference_patterns = await self.interference_calculator.calculate_interference(
|
|
quantum_corrected
|
|
)
|
|
|
|
# Optimize phase
|
|
optimized_hologram = await self.phase_optimizer.optimize_phase(interference_patterns)
|
|
|
|
return optimized_hologram
|
|
|
|
class FresnelKirchhoffIntegrator:
|
|
def __init__(self, config: HolographicConfig):
|
|
self.config = config
|
|
self.wave_propagator = WavePropagator(config)
|
|
self.field_calculator = FieldCalculator(config)
|
|
|
|
async def integrate(self, lighting_result: LightingResult) -> torch.Tensor:
|
|
"""Apply Fresnel-Kirchhoff integral"""
|
|
# Implementation for Fresnel-Kirchhoff integration
|
|
# - Wave propagation
|
|
# - Field calculation
|
|
# - Integration
|
|
# - Boundary conditions
|
|
|
|
# Calculate wave propagation
|
|
wave_propagation = await self.wave_propagator.propagate_waves(lighting_result)
|
|
|
|
# Calculate electromagnetic fields
|
|
electromagnetic_fields = await self.field_calculator.calculate_fields(wave_propagation)
|
|
|
|
# Apply Fresnel-Kirchhoff integral
|
|
hologram_field = await self.apply_fresnel_kirchhoff(electromagnetic_fields)
|
|
|
|
return hologram_field
|
|
|
|
async def apply_fresnel_kirchhoff(self, fields: torch.Tensor) -> torch.Tensor:
|
|
"""Apply Fresnel-Kirchhoff integral formula"""
|
|
# Implementation for Fresnel-Kirchhoff integral
|
|
# - Integral computation
|
|
# - Boundary evaluation
|
|
# - Field superposition
|
|
# - Phase calculation
|
|
|
|
# Initialize result
|
|
result = torch.zeros(self.config.hologram_resolution, dtype=torch.complex64)
|
|
|
|
# Apply Fresnel-Kirchhoff formula
|
|
for x in range(self.config.hologram_resolution[0]):
|
|
for y in range(self.config.hologram_resolution[1]):
|
|
# Calculate distance r
|
|
r = self.calculate_distance(x, y)
|
|
|
|
# Calculate phase factor
|
|
phase_factor = torch.exp(1j * self.config.wave_number * r) / r
|
|
|
|
# Integrate over source plane
|
|
integral = await self.integrate_source_plane(fields, x, y, phase_factor)
|
|
|
|
result[x, y] = integral
|
|
|
|
return result
|
|
|
|
def calculate_distance(self, x: int, y: int) -> float:
|
|
"""Calculate distance for Fresnel-Kirchhoff integral"""
|
|
# Implementation for distance calculation
|
|
# - Euclidean distance
|
|
# - Coordinate transformation
|
|
# - Scale factors
|
|
# - Precision handling
|
|
|
|
# Convert to physical coordinates
|
|
x_phys = x * self.config.pixel_pitch
|
|
y_phys = y * self.config.pixel_pitch
|
|
|
|
# Calculate distance
|
|
distance = np.sqrt(x_phys**2 + y_phys**2 + self.config.viewing_distance**2)
|
|
|
|
return distance
|
|
|
|
class QuantumCorrector:
|
|
def __init__(self, config: HolographicConfig):
|
|
self.config = config
|
|
self.quantum_phase_calculator = QuantumPhaseCalculator(config)
|
|
self.uncertainty_corrector = UncertaintyCorrector(config)
|
|
|
|
async def apply_corrections(self, hologram_field: torch.Tensor) -> torch.Tensor:
|
|
"""Apply quantum corrections to hologram"""
|
|
# Implementation for quantum corrections
|
|
# - Quantum phase calculation
|
|
# - Uncertainty correction
|
|
# - Quantum coherence
|
|
# - Entanglement effects
|
|
|
|
# Calculate quantum phase corrections
|
|
quantum_phase = await self.quantum_phase_calculator.calculate_phase_corrections(
|
|
hologram_field
|
|
)
|
|
|
|
# Apply uncertainty corrections
|
|
uncertainty_corrected = await self.uncertainty_corrector.apply_uncertainty_corrections(
|
|
hologram_field
|
|
)
|
|
|
|
# Apply quantum phase
|
|
quantum_corrected = uncertainty_corrected * torch.exp(1j * quantum_phase)
|
|
|
|
return quantum_corrected
|
|
|
|
async def calculate_phase_corrections(self, hologram_field: torch.Tensor) -> torch.Tensor:
|
|
"""Calculate quantum phase corrections"""
|
|
# Implementation for quantum phase calculation
|
|
# - Quantum field theory
|
|
# - Phase accumulation
|
|
# - Quantum interference
|
|
# - Coherence effects
|
|
|
|
# Calculate quantum phase using field theory
|
|
quantum_phase = torch.zeros_like(hologram_field, dtype=torch.float32)
|
|
|
|
# Apply quantum field corrections
|
|
for i in range(hologram_field.shape[0]):
|
|
for j in range(hologram_field.shape[1]):
|
|
# Calculate quantum phase contribution
|
|
phase_contribution = await self.calculate_quantum_phase_contribution(
|
|
hologram_field, i, j
|
|
)
|
|
quantum_phase[i, j] = phase_contribution
|
|
|
|
return quantum_phase
|
|
|
|
@dataclass
|
|
class HolographicConfig:
|
|
hologram_resolution: Tuple[int, int] = (2048, 2048)
|
|
wavelength: float = 550e-9 # 550nm
|
|
pixel_pitch: float = 6.4e-6 # 6.4μm
|
|
viewing_distance: float = 0.5 # 50cm
|
|
wave_number: float = 2 * np.pi / 550e-9
|
|
max_bounces: int = 3
|
|
max_steps: int = 1000
|
|
quantum_corrections: bool = True
|
|
```
|
|
|
|
## 3. Interactive Holographic Interfaces
|
|
|
|
### 3.1 Gesture Recognition
|
|
|
|
```python
|
|
class InteractiveHolographicInterfaces:
|
|
def __init__(self, config: InteractiveConfig):
|
|
self.config = config
|
|
self.gesture_recognizer = GestureRecognizer(config)
|
|
self.voice_controller = VoiceController(config)
|
|
self.eye_tracker = EyeTracker(config)
|
|
self.haptic_feedback = HapticFeedback(config)
|
|
|
|
async def process_interaction(self, input_data: InteractionData) -> InteractionResult:
|
|
"""Process multi-modal interaction"""
|
|
# Task: Implement interactive holographic interfaces
|
|
# - Gesture recognition
|
|
# - Voice control
|
|
# - Eye tracking
|
|
# - Haptic feedback
|
|
|
|
# Recognize gestures
|
|
gestures = await self.gesture_recognizer.recognize_gestures(input_data.hand_data)
|
|
|
|
# Process voice commands
|
|
voice_commands = await self.voice_controller.process_voice(input_data.audio_data)
|
|
|
|
# Track eye movements
|
|
eye_tracking = await self.eye_tracker.track_eyes(input_data.eye_data)
|
|
|
|
# Generate haptic feedback
|
|
haptic_feedback = await self.haptic_feedback.generate_feedback(
|
|
gestures, voice_commands, eye_tracking
|
|
)
|
|
|
|
return InteractionResult(gestures, voice_commands, eye_tracking, haptic_feedback)
|
|
|
|
class GestureRecognizer:
|
|
def __init__(self, config: InteractiveConfig):
|
|
self.config = config
|
|
self.hand_tracker = HandTracker(config)
|
|
self.gesture_classifier = GestureClassifier(config)
|
|
self.real_time_processor = RealTimeProcessor(config)
|
|
self.multi_hand_support = MultiHandSupport(config)
|
|
|
|
async def recognize_gestures(self, hand_data: torch.Tensor) -> List[Gesture]:
|
|
"""Recognize gestures from hand data"""
|
|
# Implementation for gesture recognition
|
|
# - Hand tracking and recognition
|
|
# - Gesture classification
|
|
# - Real-time interaction
|
|
# - Multi-hand support
|
|
|
|
# Track hands
|
|
tracked_hands = await self.hand_tracker.track_hands(hand_data)
|
|
|
|
# Classify gestures
|
|
gestures = []
|
|
for hand in tracked_hands:
|
|
gesture = await self.gesture_classifier.classify_gesture(hand)
|
|
gestures.append(gesture)
|
|
|
|
# Process in real-time
|
|
real_time_gestures = await self.real_time_processor.process_real_time(gestures)
|
|
|
|
# Support multiple hands
|
|
multi_hand_gestures = await self.multi_hand_support.process_multi_hand(real_time_gestures)
|
|
|
|
return multi_hand_gestures
|
|
|
|
async def track_hands(self, hand_data: torch.Tensor) -> List[Hand]:
|
|
"""Track hands in real-time"""
|
|
# Implementation for hand tracking
|
|
# - Hand detection
|
|
# - Joint tracking
|
|
# - Pose estimation
|
|
# - Motion prediction
|
|
|
|
# Detect hands
|
|
detected_hands = await self.detect_hands(hand_data)
|
|
|
|
# Track joints
|
|
tracked_joints = []
|
|
for hand in detected_hands:
|
|
joints = await self.track_joints(hand)
|
|
tracked_joints.append(joints)
|
|
|
|
# Estimate poses
|
|
poses = []
|
|
for joints in tracked_joints:
|
|
pose = await self.estimate_pose(joints)
|
|
poses.append(pose)
|
|
|
|
# Predict motion
|
|
motion_prediction = await self.predict_motion(poses)
|
|
|
|
return motion_prediction
|
|
|
|
async def classify_gesture(self, hand: Hand) -> Gesture:
|
|
"""Classify gesture from hand data"""
|
|
# Implementation for gesture classification
|
|
# - Feature extraction
|
|
# - Pattern recognition
|
|
# - Classification
|
|
# - Confidence scoring
|
|
|
|
# Extract features
|
|
features = await self.extract_hand_features(hand)
|
|
|
|
# Recognize patterns
|
|
patterns = await self.recognize_patterns(features)
|
|
|
|
# Classify gesture
|
|
gesture_class = await self.classify_gesture_class(patterns)
|
|
|
|
# Calculate confidence
|
|
confidence = await self.calculate_confidence(gesture_class, patterns)
|
|
|
|
return Gesture(gesture_class, confidence)
|
|
|
|
class VoiceController:
|
|
def __init__(self, config: InteractiveConfig):
|
|
self.config = config
|
|
self.speech_recognizer = SpeechRecognizer(config)
|
|
self.natural_language_processor = NaturalLanguageProcessor(config)
|
|
self.command_interpreter = CommandInterpreter(config)
|
|
self.context_analyzer = ContextAnalyzer(config)
|
|
|
|
async def process_voice(self, audio_data: torch.Tensor) -> VoiceCommand:
|
|
"""Process voice commands"""
|
|
# Implementation for voice control
|
|
# - Speech recognition
|
|
# - Natural language processing
|
|
# - Command interpretation
|
|
# - Context awareness
|
|
|
|
# Recognize speech
|
|
speech_text = await self.speech_recognizer.recognize_speech(audio_data)
|
|
|
|
# Process natural language
|
|
processed_text = await self.natural_language_processor.process_text(speech_text)
|
|
|
|
# Interpret commands
|
|
command = await self.command_interpreter.interpret_command(processed_text)
|
|
|
|
# Analyze context
|
|
contextualized_command = await self.context_analyzer.analyze_context(command)
|
|
|
|
return contextualized_command
|
|
|
|
async def recognize_speech(self, audio_data: torch.Tensor) -> str:
|
|
"""Recognize speech from audio"""
|
|
# Implementation for speech recognition
|
|
# - Feature extraction
|
|
# - Acoustic modeling
|
|
# - Language modeling
|
|
# - Decoding
|
|
|
|
# Extract audio features
|
|
features = await self.extract_audio_features(audio_data)
|
|
|
|
# Apply acoustic model
|
|
acoustic_output = await self.apply_acoustic_model(features)
|
|
|
|
# Apply language model
|
|
language_output = await self.apply_language_model(acoustic_output)
|
|
|
|
# Decode speech
|
|
speech_text = await self.decode_speech(language_output)
|
|
|
|
return speech_text
|
|
|
|
@dataclass
|
|
class InteractiveConfig:
|
|
gesture_recognition_enabled: bool = True
|
|
voice_control_enabled: bool = True
|
|
eye_tracking_enabled: bool = True
|
|
haptic_feedback_enabled: bool = True
|
|
multi_hand_support: bool = True
|
|
real_time_processing: bool = True
|
|
confidence_threshold: float = 0.8
|
|
max_hands: int = 2
|
|
gesture_timeout: float = 2.0
|
|
```
|
|
|
|
## 4. Multi-User Holographic Collaboration
|
|
|
|
### 4.1 Shared Workspace
|
|
|
|
```python
|
|
class MultiUserHolographicCollaboration:
|
|
def __init__(self, config: CollaborationConfig):
|
|
self.config = config
|
|
self.shared_workspace = SharedWorkspace(config)
|
|
self.real_time_synchronizer = RealTimeSynchronizer(config)
|
|
self.conflict_resolver = ConflictResolver(config)
|
|
|
|
async def handle_collaboration(self, user_data: List[UserData]) -> CollaborationResult:
|
|
"""Handle multi-user collaboration"""
|
|
# Task: Implement multi-user holographic collaboration
|
|
# - Shared workspace
|
|
# - Real-time synchronization
|
|
# - Conflict resolution
|
|
# - Permission management
|
|
|
|
# Update shared workspace
|
|
workspace_update = await self.shared_workspace.update_workspace(user_data)
|
|
|
|
# Synchronize in real-time
|
|
synchronized_data = await self.real_time_synchronizer.synchronize(workspace_update)
|
|
|
|
# Resolve conflicts
|
|
resolved_data = await self.conflict_resolver.resolve_conflicts(synchronized_data)
|
|
|
|
return CollaborationResult(resolved_data)
|
|
|
|
class SharedWorkspace:
|
|
def __init__(self, config: CollaborationConfig):
|
|
self.config = config
|
|
self.workspace_manager = WorkspaceManager(config)
|
|
self.object_sharer = ObjectSharer(config)
|
|
self.permission_manager = PermissionManager(config)
|
|
self.collaboration_tracker = CollaborationTracker(config)
|
|
|
|
async def update_workspace(self, user_data: List[UserData]) -> WorkspaceUpdate:
|
|
"""Update shared workspace with user interactions"""
|
|
# Implementation for shared workspace
|
|
# - Multi-user environment
|
|
# - Real-time collaboration
|
|
# - Object sharing
|
|
# - Permission management
|
|
|
|
# Update workspace state
|
|
workspace_state = await self.workspace_manager.update_state(user_data)
|
|
|
|
# Share objects
|
|
shared_objects = await self.object_sharer.share_objects(workspace_state)
|
|
|
|
# Manage permissions
|
|
permissioned_objects = await self.permission_manager.apply_permissions(shared_objects)
|
|
|
|
# Track collaboration
|
|
collaboration_tracking = await self.collaboration_tracker.track_collaboration(
|
|
permissioned_objects
|
|
)
|
|
|
|
return WorkspaceUpdate(collaboration_tracking)
|
|
|
|
async def update_state(self, user_data: List[UserData]) -> WorkspaceState:
|
|
"""Update workspace state with user interactions"""
|
|
# Implementation for workspace state update
|
|
# - State synchronization
|
|
# - Change detection
|
|
# - Update propagation
|
|
# - Consistency maintenance
|
|
|
|
# Detect changes
|
|
changes = await self.detect_changes(user_data)
|
|
|
|
# Apply changes
|
|
updated_state = await self.apply_changes(changes)
|
|
|
|
# Validate state
|
|
validated_state = await self.validate_state(updated_state)
|
|
|
|
# Propagate updates
|
|
propagated_state = await self.propagate_updates(validated_state)
|
|
|
|
return propagated_state
|
|
|
|
class RealTimeSynchronizer:
|
|
def __init__(self, config: CollaborationConfig):
|
|
self.config = config
|
|
self.sync_manager = SyncManager(config)
|
|
self.latency_optimizer = LatencyOptimizer(config)
|
|
self.consistency_checker = ConsistencyChecker(config)
|
|
|
|
async def synchronize(self, workspace_update: WorkspaceUpdate) -> SynchronizedData:
|
|
"""Synchronize data in real-time"""
|
|
# Implementation for real-time synchronization
|
|
# - Data synchronization
|
|
# - Latency optimization
|
|
# - Consistency checking
|
|
# - Update propagation
|
|
|
|
# Synchronize data
|
|
synchronized_data = await self.sync_manager.synchronize_data(workspace_update)
|
|
|
|
# Optimize latency
|
|
optimized_data = await self.latency_optimizer.optimize_latency(synchronized_data)
|
|
|
|
# Check consistency
|
|
consistent_data = await self.consistency_checker.check_consistency(optimized_data)
|
|
|
|
return consistent_data
|
|
|
|
async def synchronize_data(self, workspace_update: WorkspaceUpdate) -> torch.Tensor:
|
|
"""Synchronize data across users"""
|
|
# Implementation for data synchronization
|
|
# - Change detection
|
|
# - Update propagation
|
|
# - Conflict detection
|
|
# - Merge strategies
|
|
|
|
# Detect changes
|
|
changes = await self.detect_changes(workspace_update)
|
|
|
|
# Propagate updates
|
|
propagated_updates = await self.propagate_updates(changes)
|
|
|
|
# Detect conflicts
|
|
conflicts = await self.detect_conflicts(propagated_updates)
|
|
|
|
# Merge changes
|
|
merged_data = await self.merge_changes(propagated_updates, conflicts)
|
|
|
|
return merged_data
|
|
|
|
class ConflictResolver:
|
|
def __init__(self, config: CollaborationConfig):
|
|
self.config = config
|
|
self.conflict_detector = ConflictDetector(config)
|
|
self.resolution_strategy = ResolutionStrategy(config)
|
|
self.version_controller = VersionController(config)
|
|
|
|
async def resolve_conflicts(self, synchronized_data: SynchronizedData) -> ResolvedData:
|
|
"""Resolve conflicts in collaborative data"""
|
|
# Implementation for conflict resolution
|
|
# - Concurrent editing
|
|
# - Version control
|
|
# - Conflict detection
|
|
# - Resolution strategies
|
|
|
|
# Detect conflicts
|
|
conflicts = await self.conflict_detector.detect_conflicts(synchronized_data)
|
|
|
|
# Apply resolution strategies
|
|
resolved_data = await self.resolution_strategy.apply_resolution(conflicts)
|
|
|
|
# Update version control
|
|
versioned_data = await self.version_controller.update_versions(resolved_data)
|
|
|
|
return versioned_data
|
|
|
|
async def detect_conflicts(self, synchronized_data: SynchronizedData) -> List[Conflict]:
|
|
"""Detect conflicts in synchronized data"""
|
|
# Implementation for conflict detection
|
|
# - Concurrent modification detection
|
|
# - Version comparison
|
|
# - Conflict classification
|
|
# - Priority assessment
|
|
|
|
conflicts = []
|
|
|
|
# Check for concurrent modifications
|
|
concurrent_modifications = await self.detect_concurrent_modifications(synchronized_data)
|
|
|
|
# Compare versions
|
|
version_conflicts = await self.compare_versions(synchronized_data)
|
|
|
|
# Classify conflicts
|
|
for conflict in concurrent_modifications + version_conflicts:
|
|
conflict_class = await self.classify_conflict(conflict)
|
|
conflicts.append(conflict_class)
|
|
|
|
return conflicts
|
|
|
|
@dataclass
|
|
class CollaborationConfig:
|
|
max_users: int = 10
|
|
sync_interval: float = 0.016 # 60 FPS
|
|
conflict_resolution_timeout: float = 5.0
|
|
permission_levels: List[str] = None
|
|
version_control_enabled: bool = True
|
|
real_time_sync: bool = True
|
|
conflict_detection_enabled: bool = True
|
|
```
|
|
|
|
---
|
|
|
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*This comprehensive holographic display implementation provides detailed guidance for deploying true holographic rendering that leverages every available channel for seamless integration.* |