38 citations · 40 across the 13 of their papers we have counts for
14 papers
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…
DiffMagicFace: Identity Consistent Facial Editing of Real Videos
Huanghao Yin, Shenkun Xu, Kanle Shi +2
Text-conditioned image editing has greatly benefitted from the advancements in Image Diffusion Models. However, extending these techniques to facial video editing introduces challe…
GaussianGrow: Geometry-aware Gaussian Growing from 3D Point Clouds with Text Guidance
Weiqi Zhang, Junsheng Zhou, Haotian Geng +4
3D Gaussian Splatting has demonstrated superior performance in rendering efficiency and quality, yet the generation of 3D Gaussians still remains a challenge without proper geometr…
4C4D: 4 Camera 4D Gaussian Splatting
Junsheng Zhou, Zhifan Yang, Liang Han +4
This paper tackles the challenge of recovering 4D dynamic scenes from videos captured by as few as four portable cameras. Learning to model scene dynamics for temporally consistent…
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…
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…
NeRFPrior: Learning Neural Radiance Field as a Prior for Indoor Scene Reconstruction
Wenyuan Zhang, Emily Yue-ting Jia, Junsheng Zhou +4
Recently, it has shown that priors are vital for neural implicit functions to reconstruct high-quality surfaces from multi-view RGB images. However, current priors require large-sc…
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…
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…
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…
Fast Learning Radiance Fields by Shooting Much Fewer Rays
Wenyuan Zhang, Ruofan Xing, Yunfan Zeng +3
Learning radiance fields has shown remarkable results for novel view synthesis. The learning procedure usually costs lots of time, which motivates the latest methods to speed up th…