activity
20162021
most citedSelf-calibrating Deep Photometric Stereo Networks

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

collaborators

9 papers

cs.CV2021

Lighting, Reflectance and Geometry Estimation from 360 Panoramic Stereo

Junxuan Li, Hongdong Li, Yasuyuki Matsushita

We propose a method for estimating high-definition spatially-varying lighting, reflectance, and geometry of a scene from 360 stereo images. Our model takes advantage of t…

cs.CV20201 cited

Descriptor-Free Multi-View Region Matching for Instance-Wise 3D Reconstruction

Takuma Doi, Fumio Okura, Toshiki Nagahara +2

This paper proposes a multi-view extension of instance segmentation without relying on texture or shape descriptor matching. Multi-view instance segmentation becomes challenging fo…

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.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.CV2018

Shape-conditioned Image Generation by Learning Latent Appearance Representation from Unpaired Data

Yutaro Miyauchi, Yusuke Sugano, Yasuyuki Matsushita

Conditional image generation is effective for diverse tasks including training data synthesis for learning-based computer vision. However, despite the recent advances in generative…

cs.CV2018

Probabilistic Plant Modeling via Multi-View Image-to-Image Translation

Takahiro Isokane, Fumio Okura, Ayaka Ide +2

This paper describes a method for inferring three-dimensional (3D) plant branch structures that are hidden under leaves from multi-view observations. Unlike previous geometric appr…