11 papers
Multi-Submap Implicit Neural SLAM with Local-to-Global Loop Closure for Large-Scale Scene Reconstruction
Tianchen Deng, Chongdi Wang, Nailin Wang +6
Neural Radiance Fields (NeRF)-based SLAM has demonstrated impressive results in small-scale scene reconstruction, yet scaling these methods to extensive, complex environments remai…
RESBev: Making BEV Perception More Robust
Lifeng Zhuo, Kefan Jin, Zhe Liu +1
Bird's-eye-view (BEV) perception has emerged as a cornerstone of autonomous driving systems, providing a structured, ego-centric representation critical for downstream planning and…
WeatherCity: Urban Scene Reconstruction with Controllable Multi-Weather Transformation
Wenhua Wu, Huai Guan, Zhe Liu +1
Editable high-fidelity 4D scenes are crucial for autonomous driving, as they can be applied to end-to-end training and closed-loop simulation. However, existing reconstruction meth…
CAD-SLAM: Consistency-Aware Dynamic SLAM with Dynamic-Static Decoupled Mapping
Wenhua Wu, Chenpeng Su, Siting Zhu +6
Recent advances in neural radiation fields (NeRF) and 3D Gaussian-based SLAM have achieved impressive localization accuracy and high-quality dense mapping in static scenes. However…
Reloc-VGGT: Visual Re-localization with Geometry Grounded Transformer
Tianchen Deng, Wenhua Wu, Kunzhen Wu +7
Visual localization has traditionally been formulated as a pair-wise pose regression problem. Existing approaches mainly estimate relative poses between two images and employ a lat…
DIAL-GS: Dynamic Instance Aware Reconstruction for Label-free Street Scenes with 4D Gaussian Splatting
Chenpeng Su, Wenhua Wu, Chensheng Peng +3
Urban scene reconstruction is critical for autonomous driving, enabling structured 3D representations for data synthesis and closed-loop testing. Supervised approaches rely on cost…