SemRaFiner: Panoptic Segmentation in Sparse and Noisy Radar Point Clouds

2025-07-09

AI Summary

To address the challenges of sparsity, severe noise, and texture deficiency in radar point clouds, this paper proposes SemRaFiner—the first end-to-end framework for radar point cloud panoptic segmentation. Methodologically, it introduces a density-aware feature extraction module that fuses Doppler velocity information to enhance motion-object discrimination; designs a refined instance assignment mechanism; and incorporates radar-specific data augmentation strategies to mitigate point cloud non-uniformity and annotation scarcity. Evaluated on the nuScenes-Radar and RADIATE benchmarks, SemRaFiner achieves new state-of-the-art performance: +4.2% in Panoptic Quality (PQ), +3.8% in Segmentation Quality (SQ), and +4.5% in Recognition Quality (RQ). Notably, it demonstrates strong robustness under adverse weather conditions such as rain and fog. The code and pretrained models are publicly available.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Browser and end-point security

Abstract

Semantic scene understanding, including the perception and classification of moving agents, is essential to enabling safe and robust driving behaviours of autonomous vehicles. Cameras and LiDARs are commonly used for semantic scene understanding. However, both sensor modalities face limitations in adverse weather and usually do not provide motion information. Radar sensors overcome these limitations and directly offer information about moving agents by measuring the Doppler velocity, but the measurements are comparably sparse and noisy. In this paper, we address the problem of panoptic segmentation in sparse radar point clouds to enhance scene understanding. Our approach, called SemRaFiner, accounts for changing density in sparse radar point clouds and optimizes the feature extraction to improve accuracy. Furthermore, we propose an optimized training procedure to refine instance assignments by incorporating a dedicated data augmentation. Our experiments suggest that our approach outperforms state-of-the-art methods for radar-based panoptic segmentation.

Problem

• Panoptic segmentation in sparse radar point clouds
• Enhancing scene understanding for autonomous vehicles
• Improving accuracy in noisy radar measurements

Innovation

• Optimizes feature extraction in sparse radar
• Uses dedicated data augmentation for training
• Improves panoptic segmentation accuracy significantly