activity
20242026
collaborators

13 papers

cs.CV2026

City-Level 3D Surface Reconstruction with Viewpoint Orientation Partitioning and Scene Completion

Liang Han, Wenyuan Zhang, Junsheng Zhou +2

Multi-view 3D surface reconstruction is a longstanding challenge in computer vision. Although recent large-scale reconstruction methods based on 3D Gaussian Splatting (3DGS) achiev…

cs.CV2026

Sparse-View Surface Reconstruction using Gaussian Splatting through High-Confidence Depth Propagation with Normal Priors

Liang Han, Bangcai Wei, Junsheng Zhou +2

3D reconstruction from sparse views is a challenging task in 3D computer vision. Recent studies on 3D Gaussian Splatting (3DGS) have achieved remarkable results with sparse views i…

cs.CV2026

Learning Bijective Surface Parameterization for Inferring Signed Distance Functions from Sparse Point Clouds with Grid Deformation

Takeshi Noda, Chao Chen, Junsheng Zhou +3

Inferring signed distance functions (SDFs) from sparse point clouds remains a challenge in surface reconstruction. The key lies in the lack of detailed geometric information in spa…

cs.CV2026

VidSplat: Gaussian Splatting Reconstruction with Geometry-Guided Video Diffusion Priors

Jimin Tang, Wenyuan Zhang, Junsheng Zhou +5

Gaussian Splatting has achieved remarkable progress in multi-view surface reconstruction, yet it exhibits notable degradation when only few views are available. Although recent eff…

cs.CV2026

VRP-UDF: Towards Unbiased Learning of Unsigned Distance Functions from Multi-view Images with Volume Rendering Priors

Wenyuan Zhang, Chunsheng Wang, Kanle Shi +2

Unsigned distance functions (UDFs) have been a vital representation for open surfaces. With different differentiable renderers, current methods are able to train neural networks to…

cs.CV2025

PFF-Net: Patch Feature Fitting for Point Cloud Normal Estimation

Qing Li, Huifang Feng, Kanle Shi +4

Estimating the normal of a point requires constructing a local patch to provide center-surrounding context, but determining the appropriate neighborhood size is difficult when deal…