74 citations · 540 across the 72 of their papers we have counts for
74 papers
City-Level 3D Surface Reconstruction with Viewpoint Orientation Partitioning and Scene Completion
Liang Han, Wenyuan Zhang, Junsheng Zhou +2
Multi-view 3D surface reconstruction is a longstanding challenge in computer vision. Although recent large-scale reconstruction methods based on 3D Gaussian Splatting (3DGS) achiev…
Sparse-View Surface Reconstruction using Gaussian Splatting through High-Confidence Depth Propagation with Normal Priors
Liang Han, Bangcai Wei, Junsheng Zhou +2
3D reconstruction from sparse views is a challenging task in 3D computer vision. Recent studies on 3D Gaussian Splatting (3DGS) have achieved remarkable results with sparse views i…
VidSplat: Gaussian Splatting Reconstruction with Geometry-Guided Video Diffusion Priors
Jimin Tang, Wenyuan Zhang, Junsheng Zhou +5
Gaussian Splatting has achieved remarkable progress in multi-view surface reconstruction, yet it exhibits notable degradation when only few views are available. Although recent eff…
PFF-Net: Patch Feature Fitting for Point Cloud Normal Estimation
Qing Li, Huifang Feng, Kanle Shi +4
Estimating the normal of a point requires constructing a local patch to provide center-surrounding context, but determining the appropriate neighborhood size is difficult when deal…
Learning Compact Latent Space for Representing Neural Signed Distance Functions with High-fidelity Geometry Details
Qiang Bai, Bojian Wu, Xi Yang +1
Neural signed distance functions (SDFs) have been a vital representation to represent 3D shapes or scenes with neural networks. An SDF is an implicit function that can query signed…
U-CAN: Unsupervised Point Cloud Denoising with Consistency-Aware Noise2Noise Matching
Junsheng Zhou, Xingyu Shi, Haichuan Song +3
Point clouds captured by scanning sensors are often perturbed by noise, which have a highly negative impact on downstream tasks (e.g. surface reconstruction and shape understanding…
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…
MonoInstance: Enhancing Monocular Priors via Multi-view Instance Alignment for Neural Rendering and Reconstruction
Wenyuan Zhang, Yixiao Yang, Han Huang +4
Monocular depth priors have been widely adopted by neural rendering in multi-view based tasks such as 3D reconstruction and novel view synthesis. However, due to the inconsistent p…
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…
Learning Bijective Surface Parameterization for Inferring Signed Distance Functions from Sparse Point Clouds with Grid Deformation
Takeshi Noda, Chao Chen, Junsheng Zhou +3
Inferring signed distance functions (SDFs) from sparse point clouds remains a challenge in surface reconstruction. The key lies in the lack of detailed geometric information in spa…
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…
Binocular-Guided 3D Gaussian Splatting with View Consistency for Sparse View Synthesis
Liang Han, Junsheng Zhou, Yu-Shen Liu +1
Novel view synthesis from sparse inputs is a vital yet challenging task in 3D computer vision. Previous methods explore 3D Gaussian Splatting with neural priors (e.g. depth priors)…
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…
Zero-Shot Scene Reconstruction from Single Images with Deep Prior Assembly
Junsheng Zhou, Yu-Shen Liu, Zhizhong Han
Large language and vision models have been leading a revolution in visual computing. By greatly scaling up sizes of data and model parameters, the large models learn deep priors wh…
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…
UDiFF: Generating Conditional Unsigned Distance Fields with Optimal Wavelet Diffusion
Junsheng Zhou, Weiqi Zhang, Baorui Ma +3
Diffusion models have shown remarkable results for image generation, editing and inpainting. Recent works explore diffusion models for 3D shape generation with neural implicit func…
Learning Continuous Implicit Field with Local Distance Indicator for Arbitrary-Scale Point Cloud Upsampling
Shujuan Li, Junsheng Zhou, Baorui Ma +2
Point cloud upsampling aims to generate dense and uniformly distributed point sets from a sparse point cloud, which plays a critical role in 3D computer vision. Previous methods ty…
Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching
Junsheng Zhou, Baorui Ma, Wenyuan Zhang +3
Cross-modality registration between 2D images from cameras and 3D point clouds from LiDARs is a crucial task in computer vision and robotic. Previous methods estimate 2D-3D corresp…
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…
Neural Gradient Learning and Optimization for Oriented Point Normal Estimation
Qing Li, Huifang Feng, Kanle Shi +3
We propose Neural Gradient Learning (NGL), a deep learning approach to learn gradient vectors with consistent orientation from 3D point clouds for normal estimation. It has excelle…