NeurIPS'25 | 博世最新D2GS：无需LiDAR的自驾场景重建方案

Core Viewpoint - The article discusses the potential of D²GS, a framework for urban scene reconstruction in autonomous driving that does not rely on LiDAR, addressing challenges associated with traditional methods that depend on multi-modal sensor inputs [3][6]. Group 1: D²GS Framework - D²GS offers a solution for urban scene reconstruction without the need for LiDAR, achieving comparable geometric priors that are denser and more accurate [3][6]. - Traditional methods face challenges such as precise spatial-temporal calibration between LiDAR and other sensors, and projection errors when sensors are misaligned [3]. Group 2: Technical Insights - The framework utilizes multi-view depth initialization of Gaussian point clouds and alternates optimization of 3DGS scenes and depth estimation during training [6]. - The approach aims to overcome calibration errors and depth projection issues commonly encountered in LiDAR-based systems [6]. Group 3: Expert Insights - Zhang Youjian, an expert in 3D reconstruction algorithms from Bosch Innovation Software Center, is featured to provide detailed analysis of the D²GS work [8].