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

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Frontiers of Computer Vision (IW-FCV 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1578))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Kwansei Gakuin University, 2-1 Gakuen, Sanda-shi, 669-1337, Hyogo, Japan

    Keisuke Sakashita & Noriko Nagata

  2. University of Nagasaki, 1-1-1 Manabino, Nagayo-cho, Nishi-Sonogi-gun, Nagasaki, 851-2195, Japan

    Kensuke Tobitani

  3. Chukyo University, 101-2 Yagoto Honmachi, Showa-ku, Nagoya-shi, 466-8666, Aichi, Japan

    Koichi Taguchi & Manabu Hashimoto

  4. Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe-shi, 657-0013, Hyogo, Japan

    Iori Tani

  5. Seinan Gakuin University, 6-2-92 Nishijin, Sawara-ku, Fukuoka, 814-8511, Japan

    Sho Hashimoto

  6. Waseda University, 1-104 Totsukamachi, Shinjuku-ku, Tokyo, 169-8050, Japan

    Kenji Katahira

Authors
  1. Keisuke Sakashita
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  2. Kensuke Tobitani
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  3. Koichi Taguchi
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  4. Manabu Hashimoto
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  5. Iori Tani
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  6. Sho Hashimoto
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  7. Kenji Katahira
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  8. Noriko Nagata
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Corresponding author

Correspondence to Noriko Nagata .

Editor information

Editors and Affiliations

  1. Aoyama Gakuin University, Kanagawa, Japan

    Kazuhiko Sumi

  2. Chosun University, Gwangju, Korea (Republic of)

    In Seop Na

  3. Aoyama Gakuin University, Kanagawa, Japan

    Naoshi Kaneko

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Sakashita, K. et al. (2022). Impression Estimation Model of 3D Objects Using Multi-View Convolutional Neural Network. In: Sumi, K., Na, I.S., Kaneko, N. (eds) Frontiers of Computer Vision. IW-FCV 2022. Communications in Computer and Information Science, vol 1578. Springer, Cham. https://doi.org/10.1007/978-3-031-06381-7_24

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  • DOI: https://doi.org/10.1007/978-3-031-06381-7_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06380-0

  • Online ISBN: 978-3-031-06381-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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