3D Point-Cloud Processing Using Panoramic Images for Object Detection

Abstract

The Remote sensing application plays a major role in real-world critical application projects. The research introduces a novel approach, "3D Point-Cloud Processing Using Panoramic Images for Object Detection," aimed at enhancing the interpretability of laser point clouds through the integration of color information derived from panoramic images. Focusing on the context of Mobile Measurement Systems (MMS), where various digital cameras are utilized, the work addresses the challenges associated with processing panoramic images offering a 360-degree view angle. The core objective is to develop a robust method for generating color point clouds by establishing a mathematical correspondence between panoramic images and laser point clouds. The collinear principle of three points guides the fusion process, involving the center of the omnidirectional multi-camera system, the image point on the sphere, and the object point. Through comprehensive experimental validation, the work confirms the accuracy of the proposed algorithm and formulas, showcasing its effectiveness in generating color point clouds within MMS. This research contributes to the present development of 3D point-cloud processing, introducing a contemporary methodology for improved object detection through the fusion of panoramic images and laser point clouds.