5 citations · 6 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
NeAF: Learning Neural Angle Fields for Point Normal Estimation
Shujuan Li, Junsheng Zhou, Baorui Ma +2
Normal estimation for unstructured point clouds is an important task in 3D computer vision. Current methods achieve encouraging results by mapping local patches to normal vectors o…
Surface Reconstruction from Point Clouds by Learning Predictive Context Priors
Baorui Ma, Yu-Shen Liu, Matthias Zwicker +1
Surface reconstruction from point clouds is vital for 3D computer vision. State-of-the-art methods leverage large datasets to first learn local context priors that are represented…
Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors
Baorui Ma, Yu-Shen Liu, Zhizhong Han
It is an important task to reconstruct surfaces from 3D point clouds. Current methods are able to reconstruct surfaces by learning Signed Distance Functions (SDFs) from single poin…
Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces
Baorui Ma, Zhizhong Han, Yu-Shen Liu +1
Reconstructing continuous surfaces from 3D point clouds is a fundamental operation in 3D geometry processing. Several recent state-of-the-art methods address this problem using neu…