title: BEVCALIB: LiDAR-Camera Calibration via Geometry-Guided Bird's-Eye View
    Representations
summary: Accurate LiDAR-camera calibration is fundamental to fusing multi-modal
  perception in autonomous driving and robotic systems. Traditional calibration
  methods require extensive data collection in controlled environments and cannot
  compensate for the transformation changes during the vehicle/robot movement. In
  this paper, we propose the first model that uses bird's-eye view (BEV) features
  to perform LiDAR camera calibration from raw data, termed BEVCALIB. To achieve
  this, we extract camera BEV features and LiDAR BEV features separately and fuse
  them into a shared BEV feature space. To fully utilize the geometric
  information from the BEV feature, we introduce a novel feature selector to
  filter the most important features in the transformation decoder, which reduces
  memory consumption and enables efficient training. Extensive evaluations on
  KITTI, NuScenes, and our own dataset demonstrate that BEVCALIB establishes a
  new state of the art. Under various noise conditions, BEVCALIB outperforms the
  best baseline in the literature by an average of (47.08%, 82.32%) on KITTI
  dataset, and (78.17%, 68.29%) on NuScenes dataset, in terms of (translation,
  rotation), respectively. In the open-source domain, it improves the best
  reproducible baseline by one order of magnitude. Our code and demo results are
  available at https://cisl.ucr.edu/BEVCalib.
paper_authors._id: 68428d18af4573dbb7cba864
paper_authors.name: Weiduo Yuan
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-06-06T06:39:23.138Z
paper_authors._id: 6526503e39fd3599e87c5c53
paper_authors.avatarUrl: /avatars/f45560d4f6bbb4f0dc06b27a46429726.svg
paper_authors.fullname: Weiduo Yuan
paper_authors.type: user
paper_authors.user: Yewandou
paper_authors._id: 68428d18af4573dbb7cba865
paper_authors.name: Jerry Li
paper_authors._id: 68428d18af4573dbb7cba866
paper_authors.name: Justin Yue
paper_authors._id: 68428d18af4573dbb7cba867
paper_authors.name: Divyank Shah
paper_authors._id: 68428d18af4573dbb7cba868
paper_authors.name: Konstantinos Karydis
paper_authors._id: 68428d18af4573dbb7cba869
paper_authors.name: Hang Qiu
paper_title: BEVCALIB: LiDAR-Camera Calibration via Geometry-Guided Bird's-Eye View
    Representations
paper_summary: Accurate LiDAR-camera calibration is fundamental to fusing multi-modal
  perception in autonomous driving and robotic systems. Traditional calibration
  methods require extensive data collection in controlled environments and cannot
  compensate for the transformation changes during the vehicle/robot movement. In
  this paper, we propose the first model that uses bird's-eye view (BEV) features
  to perform LiDAR camera calibration from raw data, termed BEVCALIB. To achieve
  this, we extract camera BEV features and LiDAR BEV features separately and fuse
  them into a shared BEV feature space. To fully utilize the geometric
  information from the BEV feature, we introduce a novel feature selector to
  filter the most important features in the transformation decoder, which reduces
  memory consumption and enables efficient training. Extensive evaluations on
  KITTI, NuScenes, and our own dataset demonstrate that BEVCALIB establishes a
  new state of the art. Under various noise conditions, BEVCALIB outperforms the
  best baseline in the literature by an average of (47.08%, 82.32%) on KITTI
  dataset, and (78.17%, 68.29%) on NuScenes dataset, in terms of (translation,
  rotation), respectively. In the open-source domain, it improves the best
  reproducible baseline by one order of magnitude. Our code and demo results are
  available at https://cisl.ucr.edu/BEVCalib.
paper_ai_summary: BEVCALIB model uses bird's-eye view features for accurate LiDAR-camera calibration from raw data, demonstrating superior performance under various noise conditions.
paper_ai_keywords: bird's-eye view BEVCALIB camera BEV features LiDAR BEV features shared BEV feature space feature selector transformation decoder KITTI NuScenes reproducible baseline