mlsanityData to 14 Oct 2026

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

arXiv 2211.03932 ↗First posted 8 Nov 2022cs.CV cs.MMcited by 4

Not on the map. Why?

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.

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

8 of 20 references.

References · 8

  1. 2020Supervised Contrastive Learningcited by 2,829
  2. 2017PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volumecited by 968
  3. 2020RELLIS-3D Dataset: Data, Benchmarks and Analysiscited by 119
  4. 2020Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Reviewcited by 116
  5. 2017RegNet: Multimodal Sensor Registration Using Deep Neural Networkscited by 69
  6. 2021CalibDNN: Multimodal Sensor Calibration for Perception Using Deep Neural Networkscited by 14
  7. 2021SemCal: Semantic LiDAR-Camera Calibration using Neural MutualInformation Estimatorcited by 7
  8. 20223D ToF LiDAR in Mobile Robotics: A Reviewcited by 3

Built on by

Cited by year

Citing papers per year: 2023: 4.

4 papers.