What Really Matters for Learning-based LiDAR-Camera Calibration

• URL: http://arxiv.org/abs/2501.16969v1
• Date: Tue, 28 Jan 2025 14:12:32 GMT
• Title: What Really Matters for Learning-based LiDAR-Camera Calibration
• Authors: Shujuan Huang, Chunyu Lin, Yao Zhao,
• Abstract summary: This paper revisits the development of learning-based LiDAR-Camera calibration. We identify the critical limitations of regression-based methods with the widely used data generation pipeline. We also investigate how the input data format and preprocessing operations impact network performance.
• Score: 50.2608502974106
• License:
• Abstract: Calibration is an essential prerequisite for the accurate data fusion of LiDAR and camera sensors. Traditional calibration techniques often require specific targets or suitable scenes to obtain reliable 2D-3D correspondences. To tackle the challenge of target-less and online calibration, deep neural networks have been introduced to solve the problem in a data-driven manner. While previous learning-based methods have achieved impressive performance on specific datasets, they still struggle in complex real-world scenarios. Most existing works focus on improving calibration accuracy but overlook the underlying mechanisms. In this paper, we revisit the development of learning-based LiDAR-Camera calibration and encourage the community to pay more attention to the underlying principles to advance practical applications. We systematically analyze the paradigm of mainstream learning-based methods, and identify the critical limitations of regression-based methods with the widely used data generation pipeline. Our findings reveal that most learning-based methods inadvertently operate as retrieval networks, focusing more on single-modality distributions rather than cross-modality correspondences. We also investigate how the input data format and preprocessing operations impact network performance and summarize the regression clues to inform further improvements.

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