# SceneCalib: Automatic Targetless Calibration of Cameras and Lidars in Autonomous Driving

arXiv: https://arxiv.org/abs/2304.05530 · HTML: https://mlsanity.com/paper/2304.05530/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [Pixel-level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments](https://mlsanity.com/paper/2103.01627/) (2021)
- [CRLF: Automatic Calibration and Refinement based on Line Feature for LiDAR and Camera in Road Scenes](https://mlsanity.com/paper/2103.04558/) (2021)
- [Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach](https://mlsanity.com/paper/2001.06175/) (2020)
- [Road is Enough! Extrinsic Calibration of Non-overlapping Stereo Camera and LiDAR using Road Information](https://mlsanity.com/paper/1902.10586/) (2019)
- [TEScalib: Targetless Extrinsic Self-Calibration of LiDAR and Stereo Camera for Automated Driving Vehicles with Uncertainty Analysis](https://mlsanity.com/paper/2202.13847/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Linear Time and Space Local Point Cloud Geometry Encoder via Vectorized Kernel Mixture (VecKM)](https://mlsanity.com/paper/2404.01568/) (2024)
- [Tensor-Based Self-Calibration of Cameras via the TrifocalCalib Method](https://mlsanity.com/paper/2509.17620/) (2025)

## Abstract (arXiv)

Accurate camera-to-lidar calibration is a requirement for sensor data fusion in many 3D perception tasks. In this paper, we present SceneCalib, a novel method for simultaneous self-calibration of extrinsic and intrinsic parameters in a system containing multiple cameras and a lidar sensor. Existing methods typically require specially designed calibration targets and human operators, or they only attempt to solve for a subset of calibration parameters. We resolve these issues with a fully automatic method that requires no explicit correspondences between camera images and lidar point clouds, allowing for robustness to many outdoor environments. Furthermore, the full system is jointly calibrated with explicit cross-camera constraints to ensure that camera-to-camera and camera-to-lidar extrinsic parameters are consistent.