4 accepted papers
Vision-based 3D semantic occupancy prediction is vital for autonomous driving, enabling unified modeling of static infrastructure and dynamic agents. Global occupancy maps serve as long-term memory priors, providing valuable historical context that enhances local perception. This is particularly imp…
VINGS-Mono is a monocular inertial Gaussian Splatting (GS) SLAM framework designed for large-scale scenes. It integrates four main components: VIO Front End, 2D Gaussian Map, NVS Loop Closure, and Dynamic Eraser. The VIO Front End processes RGB frames with dense bundle adjustment and uncertainty est…
Online dense mapping of urban scenes forms a fundamental cornerstone for scene understanding and navigation of autonomous vehicles. Recent advancements in dense mapping methods are mainly based on NeRF, whose rendering speed is too slow to meet online requirements. 3D Gaussian Splatting (3DGS), with
"Online construction of open-ended language scenes is crucial for robotic applications, where open-vocabulary interactive scene understanding is required. Recently, neural implicit representation has provided a promising direction for online interactive mapping. However, implementing open-vocabulary…