Iterative Camera-LiDAR Extrinsic Optimization via Surrogate Diffusion

TL;DR

This paper introduces an iterative diffusion-based framework that enhances camera-LiDAR extrinsic calibration accuracy and robustness by refining initial parameters through surrogate denoising, applicable to various existing methods.

Contribution

The proposed surrogate diffusion framework enables iterative refinement of calibration parameters without modifying existing methods, significantly improving accuracy and stability.

Findings

Enhanced calibration accuracy across methods

Improved robustness and stability in extrinsic estimation

Outperforms other iterative approaches in experiments

Abstract

Cameras and LiDAR are essential sensors for autonomous vehicles. The fusion of camera and LiDAR data addresses the limitations of individual sensors but relies on precise extrinsic calibration. Recently, numerous end-to-end calibration methods have been proposed; however, most predict extrinsic parameters in a single step and lack iterative optimization capabilities. To address the increasing demand for higher accuracy, we propose a versatile iterative framework based on surrogate diffusion. This framework can enhance the performance of any calibration method without requiring architectural modifications. Specifically, the initial extrinsic parameters undergo iterative refinement through a denoising process, in which the original calibration method serves as a surrogate denoiser to estimate the final extrinsics at each step. For comparative analysis, we selected four state-of-the-art calibration methods as surrogate denoisers and compared the results of our diffusion process with those of two other iterative approaches. Extensive experiments demonstrate that when integrated with our diffusion model, all calibration methods achieve higher accuracy, improved robustness, and greater stability compared to other iterative techniques and their single-step counterparts.