Dynamic Voxelization for Point Cloud Processing
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Solution Overview
Problem
Existing approaches for processing point cloud data using neural networks, such as hard voxelization, result in sub-optimal representations that are not deterministic and miss information available from different views, leading to inaccurate object detection and classification.
Innovation Solution
The use of dynamic voxelization to generate a representation of point cloud data, which preserves the complete raw data and allows for bi-directional mapping between voxels and points, enabling more accurate object detection and classification by fusing information across multiple views.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If hard voxelization is used to process point cloud data, then the processing can be performed using conventional neural networks, but the representation is not deterministic and information from different views is lost
Solution Approach 1:
The patent applies dynamic voxelization where the voxel grid structure is adapted dynamically based on the point cloud data characteristics. Instead of using a fixed hard voxelization scheme, the system dynamically determines voxel boundaries and assignments, allowing the representation to preserve complete raw point cloud information while remaining compatible with neural network processing requirements.
2Ease of manufacture
If hard voxelization is used to process point cloud data, then the processing can be performed using conventional neural networks, but the representation is not deterministic
Solution Approach 1:
The dynamic voxelization approach makes the voxelization process deterministic by dynamically adapting the voxel grid to the specific point cloud data being processed. The system deterministically assigns points to voxels based on their spatial coordinates and the dynamically determined voxel structure, eliminating the non-determinism inherent in hard voxelization while maintaining neural network compatibility.
3Measurement precision
If dynamic voxelization is used to preserve complete raw point cloud data, then object detection accuracy is improved, but the complexity of the processing system increases
Solution Approach 1:
The patent segments the point cloud data into multiple views (e.g., front, back, left, right perspectives) and processes each view separately through dynamic voxelization. This segmentation approach allows the system to manage complexity by breaking down the processing task into smaller, more manageable view-specific operations while preserving complete information from all perspectives for improved detection accuracy.
Solution Approach 2:
The patent transforms the 3D point cloud data into multi-view representations, effectively adding a dimensional aspect to the data processing. By projecting points onto different 2D planes and creating voxel grids from multiple perspectives, the system manages complexity through dimensional transformation while preserving comprehensive spatial information for accurate object detection.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing point cloud data using dynamic voxelization. When deployed within an on-board system of a vehicle, processing the point cloud data using dynamic voxelization can be used to make autonomous driving decisions for the vehicle with enhanced accuracy, for example by combining representations of point cloud data characterizing a scene from multiple views of the scene.