5 papers
PartNeXt: A Next-Generation Dataset for Fine-Grained and Hierarchical 3D Part Understanding
Penghao Wang, Yiyang He, Xin Lv +4
Understanding objects at the level of their constituent parts is fundamental to advancing computer vision, graphics, and robotics. While datasets like PartNet have driven progress…
ManiTwin: Scaling Data-Generation-Ready Digital Object Dataset to 100K
Kaixuan Wang, Tianxing Chen, Jiawei Liu +13
Learning in simulation provides a useful foundation for scaling robotic manipulation capabilities. However, this paradigm often suffers from a lack of data-generation-ready digital…
CADSpotting: Robust Panoptic Symbol Spotting on Large-Scale CAD Drawings
Fuyi Yang, Jiazuo Mu, Yanshun Zhang +7
We introduce CADSpotting, an effective method for panoptic symbol spotting in large-scale architectural CAD drawings. Existing approaches often struggle with symbol diversity, scal…
CityGo: Lightweight Urban Modeling and Rendering with Proxy Buildings and Residual Gaussians
Weihang Liu, Yuhui Zhong, Yuke Li +8
Accurate and efficient modeling of large-scale urban scenes is critical for applications such as AR navigation, UAV based inspection, and smart city digital twins. While aerial ima…
AerialGo: Walking-through City View Generation from Aerial Perspectives
Fuqiang Zhao, Yijing Guo, Siyuan Yang +6
High-quality 3D urban reconstruction is essential for applications in urban planning, navigation, and AR/VR. However, capturing detailed ground-level data across cities is both lab…