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CLOCs-R: Radar-Assisted Camera-LiDAR Fusion for Long-Range Object Detection — arXiv:2611.04300

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04300
License (as archived): CC0-1.0
Archived: 2026-11-04T19:03:08.931Z
SHA-256 of the archived text: ad79efa9cc16f78473d1d4ede1b9a4f1f3ede1155b5f63da5f59223ec37e8d04
Stable URL of this page: https://metraipolis.com/documento/dc_6cd71ac552ae

How to cite

CLOCs-R: Radar-Assisted Camera-LiDAR Fusion for Long-Range Object Detection — arXiv:2611.04300 — arXiv (en), https://arxiv.org/abs/2611.04300 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_6cd71ac552ae (Metraipolis archive dc_6cd71ac552ae, sha256 ad79efa9cc16f78473d1d4ede1b9a4f1f3ede1155b5f63da5f59223ec37e8d04)

BibTeX

@misc{dc6cd71ac552ae,
  title = {CLOCs-R: Radar-Assisted Camera-LiDAR Fusion for Long-Range Object Detection — arXiv:2611.04300},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04300},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_6cd71ac552ae; sha256 ad79efa9cc16f78473d1d4ede1b9a4f1f3ede1155b5f63da5f59223ec37e8d04},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04300. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Long-range 3D object detection remains difficult for autonomous vehicles because LiDAR returns become sparse and camera evidence weakens with distance, while automotive radar keeps returning measurements far beyond both. We present CLOCs-R, an extension of the CLOCs candidate-level fusion framework that adds radar evidence to camera-LiDAR rescoring. Radar returns are accumulated over several sweeps, compensated for ego-motion, and associated with each pre-NMS 3D candidate through a range-dependent gate. Compact features summarizing support, Doppler speed and spread, range residual, and offset are then appended to every camera-LiDAR pair before a lightweight fusion network rescores the candidates. We first verify that our fusion implementation reproduces CLOCs on KITTI. We then evaluate on the daytime clear-weather subset of the aiMotive dataset, reporting distance-binned metrics out to 200 m with frozen SECOND and Cascade R-CNN detectors. Camera-LiDAR CLOCs improves on SECOND up to 50 m but degrades beyond it. A residual scoring variant, introduced post hoc, corrects SECOND's score instead of replacing it and recovers most of this loss. Radar consistently improves BEV AP at 30-50 m (0.857 to 0.870), but beyond 50 m its gain is small (+0.003 center-distance AP), within seed variation, and CLOCs-R with residual scoring is statistically tied with LiDAR-only SECOND. Our analysis attributes the limited long-range benefit to weak separation of true and false detections by radar features, scarce far-range training positives, poor camera evidence at distance, and missing LiDAR proposals that rescoring cannot recover. We report all as-designed and post hoc results side by side.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2611.04300 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T18:39:37.487Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).