38 citations · 56 across the 6 of their papers we have counts for
6 papers
Aerial Multi-View Stereo via Adaptive Depth Range Inference and Normal Cues
Yimei Liu, Yakun Ju, Yuan Rao +4
Three-dimensional digital urban reconstruction from multi-view aerial images is a critical application where deep multi-view stereo (MVS) methods outperform traditional techniques.…
FNIN: A Fourier Neural Operator-based Numerical Integration Network for Surface-form-gradients
Jiaqi Leng, Yakun Ju, Yuanxu Duan +4
Surface-from-gradients (SfG) aims to recover a three-dimensional (3D) surface from its gradients. Traditional methods encounter significant challenges in achieving high accuracy an…
Image Gradient-Aided Photometric Stereo Network
Kaixuan Wang, Lin Qi, Shiyu Qin +4
Photometric stereo (PS) endeavors to ascertain surface normals using shading clues from photometric images under various illuminations. Recent deep learning-based PS methods often…
RMAFF-PSN: A Residual Multi-Scale Attention Feature Fusion Photometric Stereo Network
Kai Luo, Yakun Ju, Lin Qi +2
Predicting accurate normal maps of objects from two-dimensional images in regions of complex structure and spatial material variations is challenging using photometric stereo metho…
Deep Learning Methods for Calibrated Photometric Stereo and Beyond
Yakun Ju, Kin-Man Lam, Wuyuan Xie +3
Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensi…
Incorporating Lambertian Priors into Surface Normals Measurement
Yakun Ju, Muwei Jian, Shaoxiang Guo +3
The goal of photometric stereo is to measure the precise surface normal of a 3D object from observations with various shading cues. However, non-Lambertian surfaces influence the m…