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20182022
most citedS-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint

11 citations · 20 across the 5 of their papers we have counts for

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cs.CV20221 cited

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…

cs.CV202211 cited

S-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint

Wenqi Yang, Guanying Chen, Chaofeng Chen +2

In this paper, we address the "dual problem" of multi-view scene reconstruction in which we utilize single-view images captured under different point lights to learn a neural scene…

cs.CV2022

JIFF: Jointly-aligned Implicit Face Function for High Quality Single View Clothed Human Reconstruction

Yukang Cao, Guanying Chen, Kai Han +2

This paper addresses the problem of single view 3D human reconstruction. Recent implicit function based methods have shown impressive results, but they fail to recover fine face de…

cs.CV2021

HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset

Guanying Chen, Chaofeng Chen, Shi Guo +3

High dynamic range (HDR) video reconstruction from sequences captured with alternating exposures is a very challenging problem. Existing methods often align low dynamic range (LDR)…

cs.CV20203 cited

Deep Photometric Stereo for Non-Lambertian Surfaces

Guanying Chen, Kai Han, Boxin Shi +2

This paper addresses the problem of photometric stereo, in both calibrated and uncalibrated scenarios, for non-Lambertian surfaces based on deep learning. We first introduce a full…

cs.CV2019

Learning Transparent Object Matting

Guanying Chen, Kai Han, Kwan-Yee K. Wong

This paper addresses the problem of image matting for transparent objects. Existing approaches often require tedious capturing procedures and long processing time, which limit thei…