The Interestingness of 3D Shapes

Manfred Lau; Luther Power

doi:10.1145/3385955.3407925

The Interestingness of 3D Shapes

Manfred Lau
, Luther Power

School of Creative Media

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

The perception of shapes and images are important areas that have been explored in previous research. While the problem of the interestingness of images has been explored, there is no previous work in the interestingness of 3D shapes to the best of our knowledge. In this paper, we study this novel problem.We collect data on how humans perceive the interestingness of 3D shapes and analyze the data to gain insights on what makes a shape interesting. We show that a function can be learned to autonomously predict an interestingness score given a new shape and demonstrate some interestingness-guided 3D shape applications.

Original language	English
Title of host publication	Proceedings SAP 2020
Subtitle of host publication	ACM Symposium on Applied Perception
Publisher	Association for Computing Machinery
ISBN (Print)	978-1-4503-7618-1
DOIs	https://doi.org/10.1145/3385955.3407925
Publication status	Published - Sept 2020
Event	SAP 2020 - ACM Symposium on Applied Perception - Virtual conference Duration: 17 Sept 2020 → 18 Sept 2020 https://sap.acm.org/2020/

Conference

Conference	SAP 2020 - ACM Symposium on Applied Perception
Period	17/09/20 → 18/09/20
Internet address	https://sap.acm.org/2020/

Bibliographical note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Research Keywords

3D shapes
interestingness
3D models

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10.1145/3385955.3407925

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@inproceedings{48ebc9dee33f44d88c9b16bfdc21a66c,

title = "The Interestingness of 3D Shapes",

abstract = "The perception of shapes and images are important areas that have been explored in previous research. While the problem of the interestingness of images has been explored, there is no previous work in the interestingness of 3D shapes to the best of our knowledge. In this paper, we study this novel problem.We collect data on how humans perceive the interestingness of 3D shapes and analyze the data to gain insights on what makes a shape interesting. We show that a function can be learned to autonomously predict an interestingness score given a new shape and demonstrate some interestingness-guided 3D shape applications.",

keywords = "3D shapes, interestingness, 3D models",

author = "Manfred Lau and Luther Power",

note = "Research Unit(s) information for this publication is provided by the author(s) concerned.; SAP 2020 - ACM Symposium on Applied Perception ; Conference date: 17-09-2020 Through 18-09-2020",

year = "2020",

month = sep,

doi = "10.1145/3385955.3407925",

language = "English",

isbn = "978-1-4503-7618-1",

booktitle = "Proceedings SAP 2020",

publisher = "Association for Computing Machinery",

address = "United States",

url = "https://sap.acm.org/2020/",

}

TY - GEN

T1 - The Interestingness of 3D Shapes

AU - Lau, Manfred

AU - Power, Luther

N1 - Research Unit(s) information for this publication is provided by the author(s) concerned.

PY - 2020/9

Y1 - 2020/9

N2 - The perception of shapes and images are important areas that have been explored in previous research. While the problem of the interestingness of images has been explored, there is no previous work in the interestingness of 3D shapes to the best of our knowledge. In this paper, we study this novel problem.We collect data on how humans perceive the interestingness of 3D shapes and analyze the data to gain insights on what makes a shape interesting. We show that a function can be learned to autonomously predict an interestingness score given a new shape and demonstrate some interestingness-guided 3D shape applications.

AB - The perception of shapes and images are important areas that have been explored in previous research. While the problem of the interestingness of images has been explored, there is no previous work in the interestingness of 3D shapes to the best of our knowledge. In this paper, we study this novel problem.We collect data on how humans perceive the interestingness of 3D shapes and analyze the data to gain insights on what makes a shape interesting. We show that a function can be learned to autonomously predict an interestingness score given a new shape and demonstrate some interestingness-guided 3D shape applications.

KW - 3D shapes

KW - interestingness

KW - 3D models

UR - https://www.scopus.com/pages/publications/85092374286

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85092374286&origin=recordpage

U2 - 10.1145/3385955.3407925

DO - 10.1145/3385955.3407925

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-4503-7618-1

BT - Proceedings SAP 2020

PB - Association for Computing Machinery

T2 - SAP 2020 - ACM Symposium on Applied Perception

Y2 - 17 September 2020 through 18 September 2020

ER -

The Interestingness of 3D Shapes

Abstract

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Research Keywords

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GRF: Task-aware Visual Mesh Saliency

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The Interestingness of 3D Shapes

Abstract

Conference

Bibliographical note

Research Keywords

Access Output

Other Access Options

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Other Files and Links

Fingerprint

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GRF: Task-aware Visual Mesh Saliency

Cite this