3D Object Detection Using Segmented Point Clouds
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Solution Overview
Problem
Existing methods for detecting objects in environments using point clouds are inefficient for large grids or small object detection, and struggle with occlusion handling and accurate localization, especially in autonomous vehicle applications.
Innovation Solution
A system that uses a segmentation neural network to rapidly discriminate foreground from background points, followed by sparse convolutional neural networks on likely foreground points, optimizing processing efficiency and accuracy by applying a lightweight 2D image backbone and fusing segmented foreground points from temporal sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense convolutions are applied to large grids for object detection, then detection coverage is improved, but computational efficiency deteriorates
Solution Approach 1:
The patent divides the point cloud data into multiple local regions or grids, processing each region separately with sparse convolutions rather than applying dense convolutions to the entire large grid. This segmentation allows efficient processing while maintaining detection coverage across the full scene.
Solution Approach 2:
The patent applies different processing strategies to different regions: sparse convolutions are used in regions with fewer points or lower object density, while denser processing is applied only where necessary. This local adaptation optimizes computational efficiency while preserving detection accuracy in critical areas.
2Productivity
If sparse convolutions are applied to all points, then computational efficiency is improved, but detection accuracy for small objects deteriorates
Solution Approach 1:
The patent performs preliminary processing steps before sparse convolutions, including point cloud downsampling with density preservation, feature extraction at multiple scales, and region-of-interest identification. These preliminary actions ensure that small objects are not lost before the efficient sparse convolution processing begins.
Solution Approach 2:
The patent processes point cloud data at multiple scales and dimensions, applying sparse convolutions not only in 3D space but also across scale dimensions and feature spaces. This multi-dimensional approach allows small objects to be detected at appropriate scales while maintaining overall computational efficiency.
3Device complexity
If traditional methods are used for occlusion handling, then implementation simplicity is maintained, but occlusion handling accuracy deteriorates
Solution Approach 1:
The patent introduces intermediate feature representations and multi-scale feature fusion mechanisms that act as mediators between occluded objects and the detection algorithm. These intermediaries preserve information about occluded regions through feature aggregation and fusion, improving occlusion handling without requiring complex post-processing.
4Measurement precision
If full point cloud processing is performed, then completeness of detection is improved, but processing time for large scenes deteriorates
Solution Approach 1:
The patent segments the large point cloud scene into multiple manageable grids or regions, processing each region independently with sparse convolutions. This segmentation maintains detection completeness across the entire scene while reducing the processing time required for each individual region, allowing parallel processing optimization.
Data Source
AI summary
Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for performing object detection. The system obtains a respective range image corresponding to each point cloud in a set of point clouds captured by one or more sensors. The system processes each range image using a segmentation neural network to generate range image features and a segmentation output. The system generates a feature representation of the set of point clouds from only the feature representations of the foreground points. The system processes the feature representation of the set of point clouds using a prediction neural network to generate a prediction characterizing the set of point clouds.