Point Cloud Segmentation Training With Sparse Component Labels
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Solution Overview
Problem
Fully supervised training of semantic segmentation neural networks for point clouds in autonomous vehicles is challenging due to the large number of points requiring manual annotation, which is infeasible or overly time-intensive, and underrepresented object categories pose difficulties in generating accurate semantic segmentation.
Innovation Solution
A method that divides point clouds into components, annotates a small number of points per component, and trains a neural network using component-wise labels and derived labels from fused point clouds, leveraging knowledge distillation to improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fully supervised training is used for semantic segmentation neural networks, then segmentation accuracy is improved, but annotation time and complexity increase significantly
Solution Approach 1:
The patent divides the point cloud into multiple components (e.g., ground, sky, buildings, vehicles) and applies different annotation strategies to each component. This segmentation allows selective annotation of only certain components rather than all points, significantly reducing annotation time while maintaining segmentation accuracy for critical components.
Solution Approach 2:
The patent implements partial supervision by annotating only a subset of points rather than all points in the point cloud. Specifically, it annotates points in selected components (such as ground and sky) while leaving other components unannotated, allowing the network to learn effective segmentation from partial labels.
2Reliability
If all points in point cloud are annotated, then complete ground truth is obtained, but annotation becomes infeasible for large numbers of points
Solution Approach 1:
The patent segments the point cloud into distinct components and selectively annotates only specific components (e.g., ground, sky) while leaving others unannotated. This makes annotation feasible for large point clouds by reducing the total number of points requiring manual labeling.
Solution Approach 2:
The system uses automatically generated component labels (from algorithms that identify ground, sky, and other components) to serve as pseudo-ground truth for unannotated regions. This self-generated labeling reduces dependence on manual annotation while maintaining reasonable ground truth coverage.
3Measurement precision
If manual annotation of all points is performed, then accurate category labels are obtained, but the process becomes overly time intensive
Solution Approach 1:
By dividing the point cloud into components and selectively annotating only certain components, the patent maintains category label accuracy for important regions while dramatically improving annotation efficiency through reduced workload.
Solution Approach 2:
The patent applies partial annotation by labeling only a portion of points (those in selected components) rather than all points, achieving sufficient category label accuracy for training while vastly improving annotation productivity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a semantic segmentation neural network for point clouds. One of the methods includes: obtaining a plurality of training points divided into a respective plurality of components; obtaining, for each of the respective plurality of components, data identifying a ground truth category for one or more labeled point; processing each training points using a semantic segmentation neural network to generate a semantic segmentation that includes a respective score for each of the plurality of categories; determining a gradient of a loss function that penalizes the semantic segmentation neural network for generating, for points in the component, non-zero scores for categories that are not the ground truth category for any labeled point in the component; and updating, using the gradient, the parameters of the semantic segmentation neural network.