3 citations · 3 across the 6 of their papers we have counts for
6 papers · 1 filter
Volume Rendering with Metric--Phase Fields for Thin Structures and Sign-Ambiguous Geometry
Junkai Deng, Jiayi Kong, Xuhui Chen +5
Neural implicit surface methods that couple volume rendering with signed distance functions (SDFs), such as VolSDF and NeuS, achieve high-quality multi-view reconstruction for wate…
Projective Normal Fields: A Convex Optimization Method for Constructing Smooth UDFs
Jiayi Kong, Chen Zong, Fei Hou +3
Constructing a smooth approximation of an unsigned distance field (UDF) from a raw point cloud is challenging because the input provides neither surface connectivity nor consistent…
Metric--Phase Fields: Decoupling Distance and Sign for Thin-Structure Reconstruction from Unoriented Point Clouds
Jiayi Kong, Xuhui Chen, Chen Zong +4
Neural Signed Distance Functions (SDFs) excel at reconstructing watertight manifolds but fail on thin structures and open boundaries due to strict inside--outside constraints. Conv…
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…
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…
Do Not DeepFake Me: Privacy-Preserving Neural 3D Head Reconstruction Without Sensitive Images
Jiayi Kong, Xurui Song, Shuo Huai +3
While 3D head reconstruction is widely used for modeling, existing neural reconstruction approaches rely on high-resolution multi-view images, posing notable privacy issues. Indivi…