3D Point Cloud Detection Using Grid-Based Key-Points and Hierarchical Convolution
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Solution Overview
Problem
Existing point cloud detection methods face challenges in achieving real-time processing speeds and balancing computation efficiency with accuracy, especially on edge devices with limited resources.
Innovation Solution
A novel framework for 3D point cloud detection using grid-based key-points extraction and hierarchical point-wise voxelization, which applies 3D convolution in a point-wise fashion to preserve point cloud geometry and reduce the reliance on expensive MLPs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If grid-based methods transform point cloud into regular grids representations, then computation efficiency is improved, but information loss occurs and manufacturing precision deteriorates
Solution Approach 1:
The method segments the point cloud processing into two distinct stages: (1) grid-based key-point extraction for efficient downsampling and feature aggregation, and (2) point-wise convolution for precise location feature learning. This segmentation allows each stage to optimize for its specific function, resolving the contradiction between computation efficiency and location precision.
Solution Approach 2:
The patent introduces point-wise convolution as a new dimensional approach that operates on individual points while leveraging grid-based key-points as anchors. This adds a new dimension of processing that combines the efficiency of grid-based methods with the precision of point-wise methods, rather than choosing one over the other.
2Manufacturing precision
If point-wise methods directly take raw points as input, then location precision is improved, but device complexity increases
Solution Approach 1:
The grid-based key-point extraction serves as a preliminary action that preprocesses the point cloud by identifying representative key-points and aggregating features in grid cells. This preliminary processing reduces the complexity burden on the subsequent point-wise convolution stage, allowing precise location feature learning without excessive computational complexity.
3Manufacturing precision
If point-wise methods use complicated MLPs, then manufacturing precision is improved, but productivity deteriorates
Solution Approach 1:
The patent merges grid-based methods and point-wise methods into a unified framework where grid-based key-point extraction and point-wise convolution work synergistically. This combination achieves impressive accuracy while significantly improving inference speed compared to using complicated MLPs alone, as the grid-based component pre-processes and aggregates features efficiently.
4Productivity
If grid-based methods are used, then productivity is improved, but loss of information increases
Solution Approach 1:
Grid-based key-points serve as intermediaries that bridge the gap between raw point cloud data and final detection results. They aggregate features from multiple points while preserving location information, acting as a mediator that reduces information loss during the transformation from point cloud to detection output.
Data Source
AI summary
A method for performing a 3D point cloud object detection is provided. The method includes: receiving an object data comprising a point cloud of a plurality of sensed points; performing a grid-based key-points extraction on raw data points of the point cloud, so as to obtain one or more key-points; performing a hierarchical point-wise convolution on the raw data points of the point cloud according to the key-points, so as to obtain a plurality of target point-wise features; performing a location-aware 3D RoI pooling operation according to the target point-wise features, so as to obtain pooled point-wise features and pooled 3D voxels corresponding to the pooled point-wise features; performing 3D convolution operations on the pooled 3D voxels, so as to obtain represent data points, wherein each of the represent data points includes a represent feature; and inputting the represent data points into multi-layer perceptrons to obtain final predictions.