Rendering-Enhanced Automatic Image-to-Point Cloud Registration for Roadside Scenes

Abstract

Prior point cloud provides 3D environmental context, which enhances the capabilities of monocular camera in downstream vision tasks, such as 3D object detection, via data fusion. However, the absence of accurate and automated registration methods for estimating camera extrinsic parameters in roadside scene point clouds notably constrains the potential applications of roadside cameras. This paper proposes a novel approach for the automatic registration between prior point clouds and images from roadside scenes. The main idea involves rendering photorealistic grayscale views taken at specific perspectives from the prior point cloud with the help of their features like RGB or intensity values. These generated views can reduce the modality differences between images and prior point clouds, thereby improve the robustness and accuracy of the registration results. Particularly, we specify an efficient algorithm, named neighbor rendering, for the rendering process. Then we introduce a method for automatically estimating the initial guess using only rough guesses of camera's position. At last, we propose a procedure for iteratively refining the extrinsic parameters by minimizing the reprojection error for line features extracted from both generated and camera images using Segment Anything Model (SAM). We assess our method using a self-collected dataset, comprising eight cameras strategically positioned throughout the university campus. Experiments demonstrate our method's capability to automatically align prior point cloud with roadside camera image, achieving a rotation accuracy of 0.202 degrees and a translation precision of 0.079m. Furthermore, we validate our approach's effectiveness in visual applications by substantially improving monocular 3D object detection performance.