82 research outputs found
制約付き回帰に基づく照度差ステレオ
学位の種別: 課程博士審査委員会委員 : (主査)東京大学准教授 山﨑 俊彦, 東京大学教授, 相澤 清晴, 東京大学教授 池内 克史, 東京大学教授 佐藤 真一, 東京大学教授 佐藤 洋一, 東京大学教授 苗村 健University of Tokyo(東京大学
PS-FCN: A Flexible Learning Framework for Photometric Stereo
This paper addresses the problem of photometric stereo for non-Lambertian
surfaces. Existing approaches often adopt simplified reflectance models to make
the problem more tractable, but this greatly hinders their applications on
real-world objects. In this paper, we propose a deep fully convolutional
network, called PS-FCN, that takes an arbitrary number of images of a static
object captured under different light directions with a fixed camera as input,
and predicts a normal map of the object in a fast feed-forward pass. Unlike the
recently proposed learning based method, PS-FCN does not require a pre-defined
set of light directions during training and testing, and can handle multiple
images and light directions in an order-agnostic manner. Although we train
PS-FCN on synthetic data, it can generalize well on real datasets. We further
show that PS-FCN can be easily extended to handle the problem of uncalibrated
photometric stereo.Extensive experiments on public real datasets show that
PS-FCN outperforms existing approaches in calibrated photometric stereo, and
promising results are achieved in uncalibrated scenario, clearly demonstrating
its effectiveness.Comment: ECCV 2018: https://guanyingc.github.io/PS-FC
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