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Real-Time Radar Semantic and Panoptic Scene Understanding

Santosh Parit, Julia Hindel, Rohit Mohan, Georgi Pramatarov, Paul M. Newman, Daniele De Martini, Abhinav Valada · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26816 · cited by 0 works (OpenAlex) · open-access PDF

Korean

강력한 네트워크 세그먼트 세팅 이해는 다양한 환경 조건에서 안전한 자율 운전을 위해 필수적입니다. 카메라와 LiDAR가 불리한 날씨와 가난한 조명에서 악화하는 동안, 주파수 모듈화 연속파 (FMCW) 스핀 레이더는 확장된 범위에서 신뢰할 수있는 360 ° 인식을 제공합니다. 그러나 실시간 FMCW 레이더 장면 이해는 복잡한 신호 반환, 제한된 시간 해상도 및 적당한 인용 데이터 세트로 인해 도전적입니다. 우리는 FMCW 레이더 스트리밍 패노 옵틱 분할을위한 새로운 프레임 워크인 StreamRPS를 도입합니다. LiDAR 장면 완료를 통해 자동 레이블 생성 파이프라인과 실시간 스트리밍 네

Machine translation (Meta M2M100); the English original follows.

Abstract

Robust scene understanding under varying environmental conditions is essential for safe autonomous driving. While cameras and LiDAR degrade in adverse weather and poor lighting, Frequency-Modulated Continuous-Wave (FMCW) spinning radar provides dependable 360° perception at extended ranges with minimal compute cost. However, real-time FMCW radar scene understanding is challenging due to complex signal returns, limited temporal resolution, and scarce annotated datasets. We introduce StreamRPS, a novel framework for streaming FMCW radar panoptic segmentation, comprising an automated label-generation pipeline via LiDAR scene completion and a real-time streaming network for semantic and panoptic segmentation. Our encoder-decoder streaming architecture comprises a radar-specific hierarchical Transformer encoder and task-specific decoders for semantic or panoptic prediction. Our encoder introduces a stem to capture range-dependent signatures, anisotropic patch embeddings to preserve angular resolution, and axial polar attention to separately model the periodic azimuth and bounded range axes. We further introduce azimuth roll rotation augmentation to simulate ego-vehicle rotation during training. Comprehensive experiments on the SAX and BOREAS datasets validate both our auto-labeling methodology and streaming segmentation network against established baselines. We outperform the strongest baseline by at least 1.4pp while processing sectors at over 400 FPS in a 4-sector streaming setting. We additionally provide manual annotations for 100 SAX radar scans, enabling independent evaluation of the entire framework.

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Topics: Computer science, Lidar, Computer vision, Artificial intelligence, Radar, Remote sensing

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