Impression Estimation Model of 3D Objects Using Multi-View Convolutional Neural Network

Keisuke Sakashita, Kensuke Tobitani, Koichi Taguchi, Manabu Hashimoto, Iori Tani, Sho Hashimoto, Kenji Katahira, Noriko Nagata*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The ultimate goal of this study is to provide intuitive design support for 3D objects. As a first attempt, we propose a method for estimating impressions of common 3D objects with various characteristics. Although many studies have been conducted to estimate objects’ aesthetics, not enough research has been conducted to estimate the various impressions of objects necessary for design support. The data set of human impressions of 3D objects is constructed based on psychological methods. To account for the variability in people’s ratings, the distribution of ratings is represented by a histogram. By learning the distribution of impression ratings, with the estimation model, we can realize an impression estimation model with high estimation accuracy. In the accuracy validation experiment, the proposed method’s estimated results (estimated impression distribution) showed a moderate to high positive correlation with the distribution of human impressions. In addition, we confirmed that the proposed method has greater estimation accuracy than previous studies and that it captures the tendency for variation in people’s impression evaluations (the global tendency of impression distribution). Furthermore, visual confirmation of the relationship between the estimation results of the constructed impression estimation model and 3D objects suggests that the proposed method is capable of identifying the main physical features associated with impression words, confirming the proposed method’s validity.

Original languageEnglish
Title of host publicationFrontiers of Computer Vision - 28th International Workshop, IW-FCV 2022, Revised Selected Papers
EditorsKazuhiko Sumi, In Seop Na, Naoshi Kaneko
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783031063800
Publication statusPublished - 2022
Event28th International Workshop on Frontiers of Computer Vision, IW-FCV 2022 - Virtual, Online
Duration: 2022 Feb 212022 Feb 22

Publication series

NameCommunications in Computer and Information Science
Volume1578 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference28th International Workshop on Frontiers of Computer Vision, IW-FCV 2022
CityVirtual, Online


  • Aesthetic concepts
  • DNN
  • Impression estimation model
  • Kansei
  • Multi-viewpoint images

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)


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