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Deep Photometric Stereo Networks for Determining Surface Normal and Reflectances

This article presents a photometric stereo method based on deep learning. One of the major difficulties in photometric stereo is designing an appropriate reflectance model that is both capable of representing real-world reflectances and computationally tractable for deriving surface normal. Unlike previous photometric stereo methods that rely on a simplified parametric image formation model, such as the Lambert’s model, the proposed method aims at establishing a flexible mapping between complex reflectance observations and surface normal using a deep neural network. In addition, the proposed method predicts the reflectance, which allows us to understand surface materials and to render the scene under arbitrary lighting conditions. As a result, we propose a deep photometric stereo network (DPSN) that takes reflectance observations under varying light directions and infers the surface normal and reflectance in a per-pixel manner. To make the DPSN applicable to real-world scenes, a dataset of measured BRDFs (MERL BRDF dataset) has been used for training the network. Evaluation using simulation and real-world scenes shows the effectiveness of the proposed approach in estimating both surface normal and reflectances.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.44 No.1 January 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 114-128

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

BENTUK GALAKSI
SURFACE NORMAL
PHOTOMETRIC STEREO

Info Detail Spesifik

DOI: 10.1109/TPAMI.2020.3005219

Pernyataan Tanggungjawab

Hiroaki Santo, Masaki Samejima, Yusuke Sugano, Boxin Shi, Yasuyuki Matsushita

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