# P2O-Calib: Camera-LiDAR Calibration Using Point-Pair Spatial Occlusion Relationship

arXiv: https://arxiv.org/abs/2311.02413 · HTML: https://mlsanity.com/paper/2311.02413/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 4 Nov 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Virtual KITTI 2](https://mlsanity.com/paper/2001.10773/) (2020)
- [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 for Lidar-Stereo Vehicle Sensor Setups](https://mlsanity.com/paper/1705.04085/) (2017)
- [LiDAR and Camera Calibration using Motion Estimated by Sensor Fusion Odometry](https://mlsanity.com/paper/1804.05178/) (2018)
- [Pixel-Pair Occlusion Relationship Map(P2ORM): Formulation, Inference & Application](https://mlsanity.com/paper/2007.12088/) (2020)
- [ACSC: Automatic Calibration for Non-repetitive Scanning Solid-State LiDAR and Camera Systems](https://mlsanity.com/paper/2011.08516/) (2020)
- [Temporal and Spatial Online Integrated Calibration for Camera and LiDAR](https://mlsanity.com/paper/2207.10454/) (2022)
- [Line-Circle-Square (LCS): A Multilayered Geometric Filter for Edge-Based Detection](https://mlsanity.com/paper/2008.09315/) (2020)

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

- [EdgeRegNet: Edge Feature-based Multimodal Registration Network between Images and LiDAR Point Clouds](https://mlsanity.com/paper/2503.15284/) (2025)
- [Galibr: Targetless LiDAR-Camera Extrinsic Calibration Method via Ground Plane Initialization](https://mlsanity.com/paper/2406.11599/) (2024)

## Abstract (arXiv)

The accurate and robust calibration result of sensors is considered as an important building block to the follow-up research in the autonomous driving and robotics domain. The current works involving extrinsic calibration between 3D LiDARs and monocular cameras mainly focus on target-based and target-less methods. The target-based methods are often utilized offline because of restrictions, such as additional target design and target placement limits. The current target-less methods suffer from feature indeterminacy and feature mismatching in various environments. To alleviate these limitations, we propose a novel target-less calibration approach which is based on the 2D-3D edge point extraction using the occlusion relationship in 3D space. Based on the extracted 2D-3D point pairs, we further propose an occlusion-guided point-matching method that improves the calibration accuracy and reduces computation costs. To validate the effectiveness of our approach, we evaluate the method performance qualitatively and quantitatively on real images from the KITTI dataset. The results demonstrate that our method outperforms the existing target-less methods and achieves low error and high robustness that can contribute to the practical applications relying on high-quality Camera-LiDAR calibration.