# RobustCalib: Robust Lidar-Camera Extrinsic Calibration with Consistency Learning

arXiv: https://arxiv.org/abs/2312.01085 · HTML: https://mlsanity.com/paper/2312.01085/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 2 Dec 2023.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [Deep Continuous Fusion for Multi-Sensor 3D Object Detection](https://mlsanity.com/paper/2012.10992/) (2020)
- [3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection](https://mlsanity.com/paper/2004.12636/) (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)
- [CMRNet: Camera to LiDAR-Map Registration](https://mlsanity.com/paper/1906.10109/) (2019)
- [Joint Camera Intrinsic and LiDAR-Camera Extrinsic Calibration](https://mlsanity.com/paper/2202.13708/) (2022)
- [DXQ-Net: Differentiable LiDAR-Camera Extrinsic Calibration Using Quality-aware Flow](https://mlsanity.com/paper/2203.09385/) (2022)
- [SOIC: Semantic Online Initialization and Calibration for LiDAR and Camera](https://mlsanity.com/paper/2003.04260/) (2020)
- [Extrinsic Camera Calibration with Semantic Segmentation](https://mlsanity.com/paper/2208.03949/) (2022)
- [Extrinsic Calibration of a 3D-LIDAR and a Camera](https://mlsanity.com/paper/2003.01213/) (2020)
- [UniCal: a Single-Branch Transformer-Based Model for Camera-to-LiDAR Calibration and Validation](https://mlsanity.com/paper/2304.09715/) (2023)
- [Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard](https://mlsanity.com/paper/1708.05514/) (2017)

## Built on by (most cited first, 1 of 1)

- [UniCalib: Targetless LiDAR-Camera Calibration via Probabilistic Flow on Unified Depth Representations](https://mlsanity.com/paper/2504.01416/) (2025)

## Abstract (arXiv)

Current traditional methods for LiDAR-camera extrinsics estimation depend on offline targets and human efforts, while learning-based approaches resort to iterative refinement for calibration results, posing constraints on their generalization and application in on-board systems. In this paper, we propose a novel approach to address the extrinsic calibration problem in a robust, automatic, and single-shot manner. Instead of directly optimizing extrinsics, we leverage the consistency learning between LiDAR and camera to implement implicit re-calibartion. Specially, we introduce an appearance-consistency loss and a geometric-consistency loss to minimizing the inconsitency between the attrbutes (e.g., intensity and depth) of projected LiDAR points and the predicted ones. This design not only enhances adaptability to various scenarios but also enables a simple and efficient formulation during inference. We conduct comprehensive experiments on different datasets, and the results demonstrate that our method achieves accurate and robust performance. To promote further research and development in this area, we will release our model and code.