1 citations · 3 across the 12 of their papers we have counts for
13 papers
3D Gaussian Splatting with Self-Constrained Priors for High Fidelity Surface Reconstruction
Takeshi Noda, Yu-Shen Liu, Zhizhong Han
Rendering 3D surfaces has been revolutionized within the modeling of radiance fields through either 3DGS or NeRF. Although 3DGS has shown advantages over NeRF in terms of rendering…
MoRe: Motion-aware Feed-forward 4D Reconstruction Transformer
Juntong Fang, Zequn Chen, Weiqi Zhang +4
Reconstructing dynamic 4D scenes remains challenging due to the presence of moving objects that corrupt camera pose estimation. Existing optimization methods alleviate this issue w…
MaterialRefGS: Reflective Gaussian Splatting with Multi-view Consistent Material Inference
Wenyuan Zhang, Jimin Tang, Weiqi Zhang +3
Modeling reflections from 2D images is essential for photorealistic rendering and novel view synthesis. Recent approaches enhance Gaussian primitives with reflection-related materi…
SparseRecon: Neural Implicit Surface Reconstruction from Sparse Views with Feature and Depth Consistencies
Liang Han, Xu Zhang, Haichuan Song +3
Surface reconstruction from sparse views aims to reconstruct a 3D shape or scene from few RGB images. The latest methods are either generalization-based or overfitting-based. Howev…
GaussianUDF: Inferring Unsigned Distance Functions through 3D Gaussian Splatting
Shujuan Li, Yu-Shen Liu, Zhizhong Han
Reconstructing open surfaces from multi-view images is vital in digitalizing complex objects in daily life. A widely used strategy is to learn unsigned distance functions (UDFs) by…
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…
NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function
Qing Li, Huifang Feng, Kanle Shi +4
Normal estimation for 3D point clouds is a fundamental task in 3D geometry processing. The state-of-the-art methods rely on priors of fitting local surfaces learned from normal sup…
Learning Signed Hyper Surfaces for Oriented Point Cloud Normal Estimation
Qing Li, Huifang Feng, Kanle Shi +4
We propose a novel method called SHS-Net for oriented normal estimation of point clouds by learning signed hyper surfaces, which can accurately predict normals with global consiste…