Submanifold Sparse Convolutional Networks for 3D Point Cloud Processing
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Solution Overview
Problem
Existing machine learning models are inefficient when processing spatially-sparse data, such as 3D point clouds, due to the rapid growth of active sites during convolution operations, leading to increased computational and memory requirements.
Innovation Solution
The development of submanifold sparse convolutional networks that maintain the sparsity pattern throughout the network, using novel sparse convolution operators that only process active sites, reducing the number of active sites and thus optimizing computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard dense convolutional networks are used to process spatially-sparse data, then comprehensive feature extraction is achieved, but computational complexity and memory requirements increase rapidly
Solution Approach 1:
The patent segments the convolution operation into two distinct parts: sparse convolution for processing spatially-sparse data and dense convolution for processing voxelized representations. This segmentation allows each convolution type to be applied where it is most efficient, reducing overall computational complexity while maintaining feature extraction accuracy
Solution Approach 2:
The patent applies different convolution strategies to different data representations: sparse convolution is used locally on spatially-sparse point cloud data where sparsity is preserved, while dense convolution is applied locally on voxelized representations where complete spatial coverage is needed. This local quality approach optimizes computational resources for each specific data type
2Area of stationary object
If standard dense convolutional networks are used to process spatially-sparse data, then complete spatial coverage is achieved, but memory usage increases due to rapid growth of active sites
Solution Approach 1:
The patent dynamically switches between sparse and dense convolution operations based on the data representation format. When processing spatially-sparse data, sparse convolution maintains dynamic sparsity patterns to limit active site growth. When voxelized representations are used, dense convolution provides complete spatial coverage. This dynamic approach adapts memory usage to the specific requirements of each processing stage
Solution Approach 2:
The patent introduces voxelized representations as an intermediary between spatially-sparse point cloud data and the final segmentation output. This intermediary allows the system to work with compact dense representations only when necessary, while preserving sparsity in the original data format, thereby reducing overall memory requirements
3Productivity
If sparse convolution operators are used to maintain sparsity pattern, then computational efficiency is improved, but processing capability on dense regions may be reduced
Solution Approach 1:
The patent creates a universal processing framework that handles both spatially-sparse data and voxelized representations within a single network architecture. The sparse convolutional neural network can process sparse point cloud data efficiently while also handling dense voxelized representations, making the system versatile across different data formats and processing requirements
Data Source
AI summary
In one embodiment, a method includes accessing a plurality of content objects, generating a plurality of voxelized representations for the plurality of content objects, respectively, generating one or more building blocks based on one or more sparse convolutions, which includes determining one or more active sites for each of the plurality of content objects based on the voxelized representation of each of the plurality of content objects and applying the one or more sparse convolutions to the one or more active sites, and training a machine-learning model based on a convolutional network including the one or more building blocks.