23 citations · 28 across the 11 of their papers we have counts for
12 papers
SharpNet: Enhancing MLPs to Represent Functions with Controlled Non-differentiability
Hanting Niu, Junkai Deng, Fei Hou +2
Multi-layer perceptrons (MLPs) are a standard tool for learning and function approximation, but they inherently produce globally smooth outputs. Consequently, they struggle to repr…
Voronoi-Assisted Optimization for Diffusing Unsigned Distance Fields from Unoriented Points
Jiayi Kong, Chen Zong, Junkai Deng +6
Unsigned Distance Fields (UDFs) provide a flexible representation for 3D shapes with arbitrary topology, including open and closed surfaces, orientable and non-orientable geometrie…
MIND: Material Interface Generation from UDFs for Non-Manifold Surface Reconstruction
Xuhui Chen, Fei Hou, Wencheng Wang +2
Unsigned distance fields (UDFs) are widely used in 3D deep learning due to their ability to represent shapes with arbitrary topology. While prior work has largely focused on learni…
A Divide-and-Conquer Approach for Global Orientation of Non-Watertight Scene-Level Point Clouds Using 0-1 Integer Optimization
Zhuodong Li, Fei Hou, Wencheng Wang +2
Orienting point clouds is a fundamental problem in computer graphics and 3D vision, with applications in reconstruction, segmentation, and analysis. While significant progress has…
From Transparent to Opaque: Rethinking Neural Implicit Surfaces with -NeuS
Haoran Zhang, Junkai Deng, Xuhui Chen +5
Traditional 3D shape reconstruction techniques from multi-view images, such as structure from motion and multi-view stereo, face challenges in reconstructing transparent objects. R…
Quasi-Medial Distance Field (Q-MDF): A Robust Method for Approximating and Discretizing Neural Medial Axes
Jiayi Kong, Chen Zong, Jun Luo +5
The medial axis, a lower-dimensional descriptor that captures the extrinsic structure of a shape, plays an important role in digital geometry processing. Despite its importance, co…
DCUDF2: Improving Efficiency and Accuracy in Extracting Zero Level Sets from Unsigned Distance Fields
Xuhui Chen, Fugang Yu, Fei Hou +3
Unsigned distance fields (UDFs) allow for the representation of models with complex topologies, but extracting accurate zero level sets from these fields poses significant challeng…
Details Enhancement in Unsigned Distance Field Learning for High-fidelity 3D Surface Reconstruction
Cheng Xu, Fei Hou, Wencheng Wang +3
While Signed Distance Fields (SDF) are well-established for modeling watertight surfaces, Unsigned Distance Fields (UDF) broaden the scope to include open surfaces and models with…
Diffusing Winding Gradients (DWG): A Parallel and Scalable Method for 3D Reconstruction from Unoriented Point Clouds
Weizhou Liu, Jiaze Li, Xuhui Chen +6
This paper presents a new method, Diffusing Winding Gradients (DWG), for reconstructing watertight 3D surfaces from unoriented point clouds. Our method exploits the alignment betwe…
Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Rendering
Baixin Xu, Jiangbei Hu, Fei Hou +4
The advancements in neural rendering have increased the need for techniques that enable intuitive editing of 3D objects represented as neural implicit surfaces. This paper introduc…
Robust Zero Level-Set Extraction from Unsigned Distance Fields Based on Double Covering
Fei Hou, Xuhui Chen, Wencheng Wang +2
In this paper, we propose a new method, called DoubleCoverUDF, for extracting the zero level-set from unsigned distance fields (UDFs). DoubleCoverUDF takes a learned UDF and a user…
Hierarchical Vectorization for Portrait Images
Qian Fu, Linlin Liu, Fei Hou +1
Aiming at developing intuitive and easy-to-use portrait editing tools, we propose a novel vectorization method that can automatically convert raster images into a 3-tier hierarchic…