97 citations · 291 across the 34 of their papers we have counts for
5 papers · 1 filter
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…
NeuralMPS: Non-Lambertian Multispectral Photometric Stereo via Spectral Reflectance Decomposition
Jipeng Lv, Heng Guo, Guanying Chen +2
Multispectral photometric stereo(MPS) aims at recovering the surface normal of a scene from a single-shot multispectral image captured under multispectral illuminations. Existing M…
NeIF: Representing General Reflectance as Neural Intrinsics Fields for Uncalibrated Photometric Stereo
Zongrui Li, Qian Zheng, Feishi Wang +3
Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by unknown light. Existing solutions alleviate the ambiguity by either explicitly associa…
Edge-preserving Near-light Photometric Stereo with Neural Surfaces
Heng Guo, Hiroaki Santo, Boxin Shi +1
This paper presents a near-light photometric stereo method that faithfully preserves sharp depth edges in the 3D reconstruction. Unlike previous methods that rely on finite differe…
1000x Faster Camera and Machine Vision with Ordinary Devices
Tiejun Huang, Yajing Zheng, Zhaofei Yu +13
In digital cameras, we find a major limitation: the image and video form inherited from a film camera obstructs it from capturing the rapidly changing photonic world. Here, we pres…