Voxel Grid Point Cloud Rendering for Hardware Acceleration
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Solution Overview
Problem
Current methods for rendering large-scale point cloud data are inefficient due to the use of hardware resources, especially when dealing with point-based rendering, as they require extensive processing and are not optimized for high-performance acceleration hardware, making it difficult to achieve interaction possible speed in CAD-type applications.
Innovation Solution
The method involves dividing the space into a 3D voxel grid, allocating texture planes to each voxel, orthogonally projecting points onto these planes to generate texture images, and rendering using the appropriate texture images based on the camera direction, allowing for efficient hardware acceleration and maintaining constant rendering speed regardless of the number of points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If point-based rendering is used instead of triangular mesh, then hardware efficiency is improved, but rendering speed deteriorates
Solution Approach 1:
The point cloud is divided into multiple voxels (spatial partitions), and each voxel is processed independently. This segmentation allows the rendering system to handle large-scale point clouds by processing smaller subsets in parallel, improving both hardware utilization and rendering speed.
Solution Approach 2:
The patent transforms 3D point cloud data into 2D texture images through orthogonal projection onto texture planes. This dimensionality reduction enables efficient use of 2D graphics hardware (GPUs) to render 3D point clouds, significantly improving rendering speed while maintaining hardware efficiency.
2Measurement precision
If a large number of points are used to represent the point cloud model, then representation accuracy is improved, but rendering speed deteriorates
Solution Approach 1:
The patent uses orthogonal projection to map all points onto texture planes, creating a complete representation without under-sampling. This partial action approach processes all points but does so efficiently through parallel hardware acceleration, maintaining both accuracy and speed.
Solution Approach 2:
The patent creates 2D texture image copies of the 3D point cloud data through orthogonal projection. These texture images serve as efficient representations that can be rendered rapidly using standard graphics hardware, maintaining the visual information of all original points while enabling fast rendering.
3Productivity
If under-sampling method is used to display points, then rendering speed is improved, but representation accuracy deteriorates
Solution Approach 1:
The patent extracts the essential visual information from the point cloud by projecting all points onto 2D texture planes. This extraction process captures the complete spatial distribution of points without requiring under-sampling, maintaining representation accuracy while enabling fast hardware-accelerated rendering.
4Productivity
If hierarchical data structure is used to render points at required detail level, then rendering speed is improved, but device complexity increases
Solution Approach 1:
The patent uses dynamic texture plane selection based on camera direction. The system dynamically determines which texture planes to render by comparing camera orientation with the six possible orthogonal directions, allowing efficient adaptation to different viewing angles without complex hierarchical data structures.
Data Source
AI summary
A method for rendering point cloud using a voxel grid, includes generating bounding box including all the point cloud and dividing the generated bounding box into voxels to make the voxel grid; and allocating at least one texture plane to each of the voxels of the voxel grid. Further, the method includes orthogonally projecting points within the voxel to the allocated texture planes to generate texture images; and rendering each voxel of the voxel grid by selecting one of the texture planes within the voxel by using central position of the voxel and the 3D camera position and rendering using the texture images corresponding to the selected texture plane.