Neural Point Cloud Detail Scaling for Bandwidth-Efficient 3D Viewing
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Solution Overview
Problem
Existing point cloud capture devices have limited accuracy and resolution, leading to inefficient data distribution and overwhelming bandwidth usage, which limits the practical detail level of captured content.
Innovation Solution
Utilizing neural networks to infer and hallucinate additional details for selected objects within point cloud data, adjusting sampling density based on viewing distance and perspective, and combining this with neural network models to dynamically adjust detail levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR systems capture high-density point clouds with fine detail, then measurement precision and object representation quality improve, but data processing time and computational resources increase significantly
Solution Approach 1:
The system segments the point cloud data into multiple levels of detail (LOD) representations. Different portions of the scene are processed at different detail levels based on their importance and distance from the viewer, allowing the system to maintain high measurement precision for critical areas while reducing processing time for less important areas through multi-resolution modeling techniques.
Solution Approach 2:
The system applies partial action by processing only the necessary portion of point cloud data at high detail levels. Rather than uniformly processing entire scenes at maximum resolution, the system selectively applies high-detail processing to specific regions of interest while using lower-detail representations for other areas, thereby reducing overall processing time while maintaining required measurement precision where needed.
2Loss of information
If LiDAR systems process complete high-resolution point clouds, then data completeness and accuracy improve, but bandwidth consumption and transmission time increase
Solution Approach 1:
The system dynamically adjusts the level of detail in point cloud data based on viewing distance, camera angle, and scene importance. As the viewer moves or the scene changes, the system adaptively transitions between different LOD representations, transmitting only the necessary amount of data at each moment. This dynamic approach maintains data completeness for relevant areas while minimizing bandwidth consumption by avoiding transmission of excessive detail for distant or less important regions.
Solution Approach 2:
The system applies local quality by assigning different detail levels to different spatial regions of the point cloud. Critical areas near the viewer or containing important features are processed and transmitted at high detail, while distant or less significant areas are represented at lower detail levels. This localized quality adjustment ensures data completeness where needed while reducing overall data volume and bandwidth consumption.
3Adaptability or versatility
If LiDAR systems generate multiple levels of detail for different viewing distances, then adaptability and user experience improve, but system complexity and processing overhead increase
Solution Approach 1:
The system performs preliminary action by pre-computing multiple levels of detail representations during the data capture and initial processing phase. These pre-generated LOD models are stored and can be rapidly switched between based on viewing conditions, rather than computing them in real-time as the viewer moves. This preliminary preparation reduces real-time processing overhead while maintaining high adaptability to different viewing distances and angles.
Data Source
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AI summary
Some embodiments of an example method disclosed herein may include receiving point cloud data representing one or more three-dimensional objects; receiving a viewpoint of the point cloud data; selecting a selected object from the one or more three-dimensional objects using the viewpoint; retrieving a neural network model for the selected object; generating a level of detail data for the selected object using the neural network model; and replacing, within the point cloud data, points corresponding to the selected object with the level of detail data.