3D Point Cloud Segmentation with Downsampling and Sparse Convolution
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Solution Overview
Problem
Machine-learning deep neural networks for 3D object segmentation in point clouds are computationally expensive and memory-intensive, hindering deployment in devices with low memory resources or strict latency requirements due to the sparsity and location independence of point clouds, leading to inefficient memory and computation overhead.
Innovation Solution
Implementing a submanifold sparse convolutional network (SSCN) model with a downsampling technique, such as grid simplification, to reduce the size of point clouds while maintaining accuracy, thereby reducing computational and memory overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used for 3D object segmentation in point clouds, then segmentation accuracy is improved, but computational overhead and memory usage increase significantly
Solution Approach 1:
The patent applies segmentation by dividing the point cloud processing into multiple stages: initial downsampling to reduce point cloud density, followed by multi-scale feature extraction at different resolution levels. This hierarchical segmentation allows the network to process fewer points while maintaining segmentation accuracy through progressive refinement at different scales.
Solution Approach 2:
The patent transforms the 3D point cloud data into a different dimensional representation by organizing points into hierarchical structures and multi-scale volumes. This dimensional transformation enables efficient processing by converting sparse 3D point data into structured representations that reduce computational complexity while preserving spatial relationships.
2Measurement precision
If deep neural networks are used for 3D object segmentation in point clouds, then segmentation accuracy is improved, but memory usage increases significantly
Solution Approach 1:
The patent extracts and processes only the most relevant features from the point cloud data through selective downsampling and multi-scale feature extraction. By taking out and processing only essential geometric and spatial information at different scales, the network reduces memory requirements while maintaining segmentation accuracy, avoiding processing of redundant point data.
Solution Approach 2:
The patent applies partial action by processing point clouds at multiple scales with varying levels of detail. Coarse-scale processing captures global structural information with minimal memory, while fine-scale processing focuses only on critical regions requiring detailed analysis. This partial processing approach reduces overall memory usage compared to processing all points at maximum resolution.
3Measurement precision
If point cloud data is processed in full resolution, then segmentation accuracy is maintained, but processing time increases
Solution Approach 1:
The patent applies preliminary action through pre-processing steps that downsample the point cloud and organize data into hierarchical structures before main segmentation processing. This preliminary organization reduces the computational burden of subsequent processing stages, enabling faster execution while preserving essential geometric information needed for accurate segmentation.
Solution Approach 2:
The patent implements periodic action through multi-scale processing where the network alternates between coarse-scale global structure analysis and fine-scale local detail processing. This periodic switching between different resolution levels allows efficient processing by performing comprehensive analysis at coarse scales and targeted refinement at fine scales, reducing total processing time.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a device that has a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, including downsampling a full point cloud to obtain a downsampled point cloud, wherein the downsampling reduces a data size of the full point cloud; and using a machine-learning model to assign labels for segmentation and object identification to points in the downsampled point cloud, wherein the machine-learning model is trained on the full point cloud. Other embodiments are disclosed.