5 papers
Mesh-Learner: Texturing Mesh with Spherical Harmonics
Yunfei Wan, Jianheng Liu, Chunran Zheng +2
In this paper, we present a 3D reconstruction and rendering framework termed Mesh-Learner that is natively compatible with traditional rasterization pipelines. It integrates mesh a…
GS-SDF: LiDAR-Augmented Gaussian Splatting and Neural SDF for Geometrically Consistent Rendering and Reconstruction
Jianheng Liu, Yunfei Wan, Bowen Wang +3
Digital twins are fundamental to the development of autonomous driving and embodied artificial intelligence. However, achieving high-granularity surface reconstruction and high-fid…
LVBA: LiDAR-Visual Bundle Adjustment for RGB Point Cloud Mapping
Rundong Li, Xiyuan Liu, Haotian Li +4
Point cloud maps with accurate color are crucial in robotics and mapping applications. Existing approaches for producing RGB-colorized maps are primarily based on real-time localiz…
Neural Surface Reconstruction and Rendering for LiDAR-Visual Systems
Jianheng Liu, Chunran Zheng, Yunfei Wan +3
This paper presents a unified surface reconstruction and rendering framework for LiDAR-visual systems, integrating Neural Radiance Fields (NeRF) and Neural Distance Fields (NDF) to…
Visual Localization in 3D Maps: Comparing Point Cloud, Mesh, and NeRF Representations
Lintong Zhang, Yifu Tao, Jiarong Lin +2
Recent advances in mapping techniques have enabled the creation of highly accurate dense 3D maps during robotic missions, such as point clouds, meshes, or NeRF-based representation…