Rendering-Enhanced Automatic Image-to-Point Cloud Registration for Roadside Scenes
arxiv(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要