Image-to-Point Cloud Registration using Camera Motion Generation and Monocular Depth Estimation

Abstract

Image-to-point cloud (I2P) registration plays a vital role in applications requiring accurate spatial alignment between 2D images and their corresponding 3D point clouds. Traditional I2P methods often require extensive training to generalise across diverse environments and rely on intrinsic camera parameters for accurate metric depth estimation, limiting their effectiveness in complex or unseen scenarios. To address these challenges, this study introduces a novel approach that leverages Camera Motion Generation (CMG) and Monocular Depth Estimation (MDE) for I2P registration task. CMG simulates camera movements in the up, down, left, and right directions, enabling the generation of novel view..