5 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…
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…
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…
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…
2S-UDF: A Novel Two-stage UDF Learning Method for Robust Non-watertight Model Reconstruction from Multi-view Images
Junkai Deng, Fei Hou, Xuhui Chen +2
Recently, building on the foundation of neural radiance field, various techniques have emerged to learn unsigned distance fields (UDF) to reconstruct 3D non-watertight models from…