8 papers · 1 filter
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…