3D Point Cloud Simplification for Low-Memory Object Segmentation
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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
Implement a submanifold sparse convolutional network (SSCN) model with a U-Net SSCN structure and apply downsampling techniques like grid simplification to reduce the point cloud size, using a machine-learning model trained on full point clouds for accurate segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full point cloud data is used for 3D object segmentation, then segmentation accuracy is improved, but computational overhead and memory consumption increase significantly
Solution Approach 1:
The patent applies segmentation by dividing the point cloud processing into two stages: (1) downsampling the full point cloud to create a reduced point cloud with fewer points, and (2) performing segmentation only on the reduced point cloud. This segmentation of the processing task reduces computational overhead while maintaining segmentation accuracy by preserving essential geometric features in the downsampled representation.
Solution Approach 2:
The patent extracts only the essential geometric features from the full point cloud by applying downsampling techniques (such as grid simplification or random sampling) to create a reduced point cloud. This extraction process removes redundant points while retaining the critical structural information needed for accurate segmentation, thereby reducing computational overhead without sacrificing segmentation quality.
2Measurement precision
If full point cloud data is used for 3D object segmentation, then segmentation accuracy is improved, but memory consumption increases significantly
Solution Approach 1:
The patent segments the point cloud data processing by creating a reduced point cloud that contains only the essential points needed for segmentation. By dividing the full point cloud into a manageable subset through downsampling, the memory consumption is reduced while the segmented structure preserves the geometric features necessary for accurate segmentation results.
Solution Approach 2:
The patent extracts a reduced set of points from the full point cloud using downsampling techniques, keeping only the points that contribute most to segmentation accuracy. This extraction reduces the quantity of data that needs to be stored in memory during the segmentation process, thereby lowering memory consumption while maintaining segmentation performance.
3Productivity
If point cloud size is reduced through downsampling, then computational overhead and memory consumption are reduced, but segmentation accuracy may deteriorate
Solution Approach 1:
The patent applies local quality by using different downsampling strategies for different regions of the point cloud. Instead of uniform downsampling, the method preserves higher point density in regions that are critical for segmentation accuracy (such as object boundaries or distinctive features) while applying more aggressive downsampling in less critical regions. This selective approach maintains segmentation accuracy while improving processing efficiency.
Solution Approach 2:
The patent changes the parameter of point cloud density through controlled downsampling. By adjusting the downsampling rate and using techniques like grid simplification or learned downsampling, the method transforms the point cloud to have optimal density for segmentation - not too dense to cause computational burden, not too sparse to lose geometric features. This parameter optimization balances processing efficiency and segmentation accuracy.
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.