most citedSelf-calibrating Deep Photometric Stereo Networks

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

collaborators

5 papers

cs.LG20202 cited

Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks

Yehui Tang, Yunhe Wang, Yixing Xu +4

Deep neural networks often consist of a great number of trainable parameters for extracting powerful features from given datasets. On one hand, massive trainable parameters signifi…

cs.LG20202 cited

On Positive-Unlabeled Classification in GAN

Tianyu Guo, Chang Xu, Jiajun Huang +4

This paper defines a positive and unlabeled classification problem for standard GANs, which then leads to a novel technique to stabilize the training of the discriminator in GANs.…

cs.CV20201 cited

Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materials

Min Li, Zhenglong Zhou, Zhe Wu +3

We present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo (MVPS) technique that works for general isotropic materials. Our…

cs.CV20195 cited

Self-calibrating Deep Photometric Stereo Networks

Guanying Chen, Kai Han, Boxin Shi +2

This paper proposes an uncalibrated photometric stereo method for non-Lambertian scenes based on deep learning. Unlike previous approaches that heavily rely on assumptions of speci…

cs.CV20191 cited

Face Image Reflection Removal

Renjie Wan, Boxin Shi, Haoliang Li +2

Face images captured through the glass are usually contaminated by reflections. The non-transmitted reflections make the reflection removal more challenging than for general scenes…