Zhengkang Zhou — accepted AI-conference papers

2026

BrepGaussian: CAD reconstruction from Multi-View Images with Gaussian Splatting

Jiaxing Yu, Dongyang Ren, Hangyu Xu, Zhouyuxiao Yang, Yuanqi Li, Jie Guo +2

CVPR 2026

The boundary representation (B-Rep) models a 3D solid as its explicit boundaries: trimmed corners, edges, and faces. Recovering B-Rep representation from unstructured data is a challenging and valuable task of computer vision and graphics. Recent advances in deep learning have greatly improved the r

Cited by 0 Source ↗Scholar ↗

2026

LoG3D: Ultra-High-Resolution 3D Shape Modeling via Local-to-Global Partitioning

Xinran Yang, Shuichang Lai, Jiangjing Lyu, Hongjie Li, Bowen Pan, Yuanqi Li +3

CVPR 2026

Generating high-fidelity 3D contents remains a fundamental challenge due to the complexity of representing arbitrary topologies--such as open surfaces and intricate internal structures--while preserving geometric details. Prevailing methods based on signed distance fields (SDFs) are hampered by cost

Cited by 0 Source ↗Scholar ↗