# CalibRefine: Deep Learning-Based Online Automatic Targetless LiDAR-Camera Calibration with Iterative and Attention-Driven Post-Refinement

arXiv: https://arxiv.org/abs/2502.17648 · HTML: https://mlsanity.com/paper/2502.17648/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 70 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review](https://mlsanity.com/paper/2004.05224/) (2020)
- [RegNet: Multimodal Sensor Registration Using Deep Neural Networks](https://mlsanity.com/paper/1707.03167/) (2017)
- [Pixel-level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments](https://mlsanity.com/paper/2103.01627/) (2021)
- [General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox](https://mlsanity.com/paper/2302.05094/) (2023)
- [Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups](https://mlsanity.com/paper/2101.04431/) (2021)
- [Camera-Lidar Integration: Probabilistic sensor fusion for semantic mapping](https://mlsanity.com/paper/2007.05490/) (2020)
- [Automatic extrinsic calibration between a camera and a 3D Lidar using 3D point and plane correspondences](https://mlsanity.com/paper/1904.12433/) (2019)
- [Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach](https://mlsanity.com/paper/2001.06175/) (2020)
- [LCE-Calib: Automatic LiDAR-Frame/Event Camera Extrinsic Calibration With A Globally Optimal Solution](https://mlsanity.com/paper/2303.09825/) (2023)
- [CalibFormer: A Transformer-based Automatic LiDAR-Camera Calibration Network](https://mlsanity.com/paper/2311.15241/) (2023)
- [Targetless Extrinsic Calibration of Multiple Small FoV LiDARs and Cameras using Adaptive Voxelization](https://mlsanity.com/paper/2109.06550/) (2021)
- [TransRAD: Retentive Vision Transformer for Enhanced Radar Object Detection](https://mlsanity.com/paper/2501.17977/) (2025)
- [SemCal: Semantic LiDAR-Camera Calibration using Neural MutualInformation Estimator](https://mlsanity.com/paper/2109.10270/) (2021)
- [Observability-Aware Intrinsic and Extrinsic Calibration of LiDAR-IMU Systems](https://mlsanity.com/paper/2205.03276/) (2022)
- [3D Radar and Camera Co-Calibration: A Flexible and Accurate Method for Target-based Extrinsic Calibration](https://mlsanity.com/paper/2307.15264/) (2023)
- [Online Targetless Radar-Camera Extrinsic Calibration Based on the Common Features of Radar and Camera](https://mlsanity.com/paper/2309.00787/) (2023)
- [Automatic Target-Less Camera-LiDAR Calibration From Motion and Deep Point Correspondences](https://mlsanity.com/paper/2404.17298/) (2024)
- [Deep Learning-Based Robust Multi-Object Tracking via Fusion of mmWave Radar and Camera Sensors](https://mlsanity.com/paper/2407.08049/) (2024)
- [LiDAR-Camera Calibration under Arbitrary Configurations: Observability and Methods](https://mlsanity.com/paper/1903.06141/) (2019)
- [Greedy Matching: Guarantees and Limitations](https://mlsanity.com/paper/1505.04198/) (2015)
- [Multi-FEAT: Multi-Feature Edge Alignment for Targetless Camera-LiDAR Calibration](https://mlsanity.com/paper/2207.07228/) (2022)
- [EdgeCalib: Multi-Frame Weighted Edge Features for Automatic Targetless LiDAR-Camera Calibration](https://mlsanity.com/paper/2310.16629/) (2023)

## Built on by (most cited first, 3 of 3)

- [Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategies](https://mlsanity.com/paper/2603.16952/) (2026)
- [Radar-Camera Fused Multi-Object Tracking: Online Calibration and Common Feature](https://mlsanity.com/paper/2510.20794/) (2025)
- [CLIFE: Camera-LiDAR Fusion Framework for Edge-Deployable Roadside VRU Perception](https://mlsanity.com/paper/2607.16154/) (2026)

## Abstract (arXiv)

Accurate multi-sensor calibration is essential for deploying robust perception systems in applications such as autonomous driving and intelligent transportation. Existing LiDAR-camera calibration methods often rely on manually placed targets, preliminary parameter estimates, or intensive data preprocessing, limiting their scalability and adaptability in real-world settings. In this work, we propose a fully automatic, targetless, and online calibration framework, CalibRefine, which directly processes raw LiDAR point clouds and camera images. Our approach is divided into four stages: (1) a Common Feature Discriminator that leverages relative spatial positions, visual appearance embeddings, and semantic class cues to identify and generate reliable LiDAR-camera correspondences, (2) a coarse homography-based calibration that uses the matched feature correspondences to estimate an initial transformation between the LiDAR and camera frames, serving as the foundation for further refinement, (3) an iterative refinement to incrementally improve alignment as additional data frames become available, and (4) an attention-based refinement that addresses non-planar distortions by leveraging a Vision Transformer and cross-attention mechanisms. Extensive experiments on two urban traffic datasets demonstrate that CalibRefine achieves high-precision calibration with minimal human input, outperforming state-of-the-art targetless methods and matching or surpassing manually tuned baselines. Our results show that robust object-level feature matching, combined with iterative refinement and self-supervised attention-based refinement, enables reliable sensor alignment in complex real-world conditions without ground-truth matrices or elaborate preprocessing. Code is available at https://github.com/radar-lab/Lidar_Camera_Automatic_Calibration