1 citations · 3 across the 12 of their papers we have counts for
6 papers · 1 filter
Sharpening Neural Implicit Functions with Frequency Consolidation Priors
Chao Chen, Yu-Shen Liu, Zhizhong Han
Signed Distance Functions (SDFs) are vital implicit representations to represent high fidelity 3D surfaces. Current methods mainly leverage a neural network to learn an SDF from va…
MultiPull: Detailing Signed Distance Functions by Pulling Multi-Level Queries at Multi-Step
Takeshi Noda, Chao Chen, Weiqi Zhang +3
Reconstructing a continuous surface from a raw 3D point cloud is a challenging task. Recent methods usually train neural networks to overfit on single point clouds to infer signed…
Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priors
Chao Chen, Yu-Shen Liu, Zhizhong Han
It is important to estimate an accurate signed distance function (SDF) from a point cloud in many computer vision applications. The latest methods learn neural SDFs using either a…
Neural Signed Distance Function Inference through Splatting 3D Gaussians Pulled on Zero-Level Set
Wenyuan Zhang, Yu-Shen Liu, Zhizhong Han
It is vital to infer a signed distance function (SDF) in multi-view based surface reconstruction. 3D Gaussian splatting (3DGS) provides a novel perspective for volume rendering, an…
Learning Local Pattern Modularization for Point Cloud Reconstruction from Unseen Classes
Chao Chen, Yu-Shen Liu, Zhizhong Han
It is challenging to reconstruct 3D point clouds in unseen classes from single 2D images. Instead of object-centered coordinate system, current methods generalized global priors le…
VRP-UDF: Towards Unbiased Learning of Unsigned Distance Functions from Multi-view Images with Volume Rendering Priors
Wenyuan Zhang, Chunsheng Wang, Kanle Shi +2
Unsigned distance functions (UDFs) have been a vital representation for open surfaces. With different differentiable renderers, current methods are able to train neural networks to…