Three-dimensional shape reconstruction from a single image based on feature learning

Kentaro Sakai, Yoshiaki Yasumura

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Many previous works have proposed methods for reconstructing a three-dimensional (3D) shape from a single image. Some of the methods reconstruct a 3D shape using machine learning. These methods learn the relationship between a 3D shape and a 2D image. However, they cannot learn the desirable features of 2D images for 3D reconstruction, because they use only predefined features. Therefore, this paper presents a method for reconstructing the 3D shape by learning features of a 2D image. This method reconstructs a 3D shape by using Convolutional Neural Network (CNN) for feature learning. The pooling layer and the convolutional layer of the CNN enable us to acquire spatial information of an image and automatically select the valuable feature of the image. From the experimental results using human face images, this method can reconstruct the 3D shape with better accuracy than the previous methods.

本文言語English
ホスト出版物のタイトル2018 International Workshop on Advanced Image Technology, IWAIT 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1-4
ページ数4
ISBN(電子版)9781538626153
DOI
出版ステータスPublished - 2018 5 30
イベント2018 International Workshop on Advanced Image Technology, IWAIT 2018 - Chiang Mai, Thailand
継続期間: 2018 1 72018 1 9

Other

Other2018 International Workshop on Advanced Image Technology, IWAIT 2018
国/地域Thailand
CityChiang Mai
Period18/1/718/1/9

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • コンピュータ ビジョンおよびパターン認識
  • メディア記述

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