# Enhanced Low-resolution LiDAR-Camera Calibration Via Depth Interpolation and Supervised Contrastive Learning

arXiv: https://arxiv.org/abs/2211.03932 · HTML: https://mlsanity.com/paper/2211.03932/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 8 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [RELLIS-3D Dataset: Data, Benchmarks and Analysis](https://mlsanity.com/paper/2011.12954/) (2020)
- [Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review](https://mlsanity.com/paper/2005.09830/) (2020)
- [RegNet: Multimodal Sensor Registration Using Deep Neural Networks](https://mlsanity.com/paper/1707.03167/) (2017)
- [CalibDNN: Multimodal Sensor Calibration for Perception Using Deep Neural Networks](https://mlsanity.com/paper/2103.14793/) (2021)
- [SemCal: Semantic LiDAR-Camera Calibration using Neural MutualInformation Estimator](https://mlsanity.com/paper/2109.10270/) (2021)
- [3D ToF LiDAR in Mobile Robotics: A Review](https://mlsanity.com/paper/2202.11025/) (2022)

## Built on by (most cited first, 4 of 4)

- [Deep Learning for Camera Calibration and Beyond: A Survey](https://mlsanity.com/paper/2303.10559/) (2023)
- [From Chaos to Calibration: A Geometric Mutual Information Approach to Target-Free Camera LiDAR Extrinsic Calibration](https://mlsanity.com/paper/2311.01905/) (2023)
- [TransUPR: A Transformer-based Uncertain Point Refiner for LiDAR Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2302.08594/) (2023)
- [Continuous Online Extrinsic Calibration of Fisheye Camera and LiDAR](https://mlsanity.com/paper/2306.13240/) (2023)

## Abstract (arXiv)

Motivated by the increasing application of low-resolution LiDAR recently, we target the problem of low-resolution LiDAR-camera calibration in this work. The main challenges are two-fold: sparsity and noise in point clouds. To address the problem, we propose to apply depth interpolation to increase the point density and supervised contrastive learning to learn noise-resistant features. The experiments on RELLIS-3D demonstrate that our approach achieves an average mean absolute rotation/translation errors of 0.15cm/0.33textdegree on 32-channel LiDAR point cloud data, which significantly outperforms all reference methods.