Self-Supervised Moving Object Segmentation of Sparse and Noisy Radar Point Clouds

TL;DR

This paper introduces a self-supervised learning approach for segmenting moving objects in sparse, noisy radar point clouds, enhancing performance with limited labeled data and reducing annotation costs.

Contribution

It proposes a novel contrastive clustering loss with dynamic points removal for pretraining, improving label efficiency and boosting state-of-the-art results in radar-based segmentation.

Findings

Improved segmentation accuracy after self-supervised pretraining

Enhanced label efficiency with limited annotated data

State-of-the-art performance achieved through the proposed method

Abstract

Moving object segmentation is a crucial task for safe and reliable autonomous mobile systems like self-driving cars, improving the reliability and robustness of subsequent tasks like SLAM or path planning. While the segmentation of camera or LiDAR data is widely researched and achieves great results, it often introduces an increased latency by requiring the accumulation of temporal sequences to gain the necessary temporal context. Radar sensors overcome this problem with their ability to provide a direct measurement of a point's Doppler velocity, which can be exploited for single-scan moving object segmentation. However, radar point clouds are often sparse and noisy, making data annotation for use in supervised learning very tedious, time-consuming, and cost-intensive. To overcome this problem, we address the task of self-supervised moving object segmentation of sparse and noisy radar point clouds. We follow a two-step approach of contrastive self-supervised representation learning with subsequent supervised fine-tuning using limited amounts of annotated data. We propose a novel clustering-based contrastive loss function with cluster refinement based on dynamic points removal to pretrain the network to produce motion-aware representations of the radar data. Our method improves label efficiency after fine-tuning, effectively boosting state-of-the-art performance by self-supervised pretraining.