Panoptic Segmentation of Point Clouds via Centroid-Aware Repel Loss
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Solution Overview
Problem
Current panoptic segmentation methods for point clouds, particularly in autonomous driving and robotics, are computationally inefficient and require significant memory and computing resources, especially in top-down approaches.
Innovation Solution
A clustering-based method using a neural network to extract features, identify object clusters, and selectively shift points away from other objects by determining centroids and using a centroid-aware repel loss to improve clustering efficiency, reducing confusion between instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If top-down panoptic segmentation methods are used, then object detection accuracy is improved, but computational efficiency deteriorates and memory requirements increase
Solution Approach 1:
The patent inverts the conventional top-down approach by implementing a bottom-up panoptic segmentation method. Instead of generating object proposals first and then classifying them, the system performs semantic segmentation first to classify all points, then groups points with the same class into instances. This inversion reduces computational complexity and memory requirements while maintaining segmentation accuracy.
Solution Approach 2:
The patent divides the panoptic segmentation task into two distinct phases: semantic segmentation (classifying each point into a category) and instance segmentation (grouping points of the same class into separate instances). This segmentation of the overall task allows for more efficient processing by handling classification and grouping separately, reducing the computational burden compared to traditional unified approaches.
2Productivity
If traditional clustering methods are used without point shifting, then processing speed is maintained, but clustering accuracy deteriorates due to confusion between instances
Solution Approach 1:
The patent applies preliminary action by shifting points towards their respective object centroids before performing clustering. This pre-processing step adjusts the point positions to reduce overlap and confusion between adjacent instances, making the subsequent clustering operation more accurate without significantly increasing processing time. The centroid calculation and point shifting are performed efficiently using vector operations.
Data Source
AI summary
A method and system for clustering-based panoptic segmentation of point clouds and a method of training the same are provided. Features of a point cloud that includes a plurality of points are extracted. Clusters of the plurality of points corresponding to objects from the features of the point cloud frame are identified. A subset of the plurality of points is selectively shifted using the features and the clusters of the plurality of points via a neural network that is trained to recognize a subset of points of objects that are closer to points of other objects than a distance between centroids of the corresponding objects and shift the subset of points away from the other objects.