most citedLocally Attentional SDF Diffusion for Controllable 3D Shape Generation

120 citations · 246 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20238 cited

Learning the Geodesic Embedding with Graph Neural Networks

Bo Pang, Zhongtian Zheng, Guoping Wang +1

We present GeGnn, a learning-based method for computing the approximate geodesic distance between two arbitrary points on discrete polyhedra surfaces with constant time complexity…

cs.CV20234 cited

Neural-Singular-Hessian: Implicit Neural Representation of Unoriented Point Clouds by Enforcing Singular Hessian

Zixiong Wang, Yunxiao Zhang, Rui Xu +6

Neural implicit representation is a promising approach for reconstructing surfaces from point clouds. Existing methods combine various regularization terms, such as the Eikonal and…

cs.GR2023

Visual-Guided Mesh Repair

Zhongtian Zheng, Xifeng Gao, Zherong Pan +4

Mesh repair is a long-standing challenge in computer graphics and related fields. Converting defective meshes into watertight manifold meshes can greatly benefit downstream applica…

cs.CV2023120 cited

Locally Attentional SDF Diffusion for Controllable 3D Shape Generation

Xin-Yang Zheng, Hao Pan, Peng-Shuai Wang +3

Although the recent rapid evolution of 3D generative neural networks greatly improves 3D shape generation, it is still not convenient for ordinary users to create 3D shapes and con…

cs.CV2023114 cited

OctFormer: Octree-based Transformers for 3D Point Clouds

Peng-Shuai Wang

We propose octree-based transformers, named OctFormer, for 3D point cloud learning. OctFormer can not only serve as a general and effective backbone for 3D point cloud segmentation…