1 citations · 2 across the 11 of their papers we have counts for
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CitySTAR: Structured and Topology-Aware Reasoning for Open-Vocabulary Urban 3D Grounding
Shuai Zhang, Hongye Hou, Qinghe Liu +5
3D grounding aims to localize target entities in complex scenes from natural language and plays a fundamental role in embodied perception and spatial reasoning. However, existing a…
SkyAnchor: Updating Metric-scale Aerial 3D Gaussian Scenes from Unposed Ground-View Sequences
Zhuoxiao Li, Xinyi Liu, Taoyu Wu +8
We study how to update a pre-built aerial scene with a newly captured, unposed ground-view sequence. The aerial scene already contains a reliable metric Structure-from-Motion (SfM)…
GraspFoM: Towards Reconstruction-Driven Robotic Grasping with 3D Foundation Priors
Dongli Wu, Xiaobao Wei, Hao Wang +5
Robotic grasping is a fundamental capability in robotic manipulation. Yet grasping remains challenging under partial observations. Reliable grasping depends on both local contact c…
Sat2City v2: Native 3D City Asset Generation from a Single Satellite Image
Tongyan Hua, Dongli Wu, Jinjing Zhu +5
Generating explicit textured 3D city assets from a single satellite image is important for urban simulation and digital twins. Most prior methods, however, learn 3D proxies optimiz…
Feed-Forward Gaussian Splatting from Sparse Aerial Views
Dongli Wu, Zhuoxiao Li, Tongyan Hua +4
Reconstructing large-scale urban scenes from sparse aerial views is a crucial yet challenging task. Due to biased top-down and shallow-oblique camera poses, sparse aerial captures…
UniD-Shift: Towards Unified Semantic Segmentation via Interpretable Share-Private Multimodal Decomposition
Shuai Zhang, Zhecheng Shi, Zhuxiao Li +4
Semantic segmentation of large-scale 3D point clouds is crucial for applications such as autonomous driving and urban digital twins. However, the sparse sampling pattern of LiDAR a…