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3D sketching for 3D object retrieval

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Abstract

Sketching provides the most natural way to provide a visual search query for visual object search. However, how to draw 3D sketches in a three-dimensional space and how to use a hand-drawn 3D sketch to search similar 3D models are not only interesting and novel, but also challenging research topics. In this paper, we try to answer them by initiating a novel study on 3D sketching and build a 3D sketching system which allows users to freely draw 3D sketches in the air and demonstrate its promising potentials in related applications such as collecting 3D sketch data and conducting 3D sketch-based 3D model retrieval. By utilizing the 3D sketching system, we collect a 3D sketch dataset, build a 3D sketch-based 3D model retrieval benchmark, and organize a Eurographics Shape Retrieval Contest (SHREC) track on 3D sketch-based shape retrieval based on the benchmark. We investigate 3D sketch and model matching problems and propose a novel 3D sketch-based model retrieval algorithm CNN-SBR based on Convolutional Neural Networks (CNNs) and achieve the best performance in the SHREC track. We wish that the 3D sketching system, the 3D sketch-based model retrieval benchmark, and the proposed 3D sketch-based model retrieval algorithm CNN-SBR will further promote sketch-based shape retrieval and its applications. We have made all of these publicly available on the project homepage: http://orca.st.usm.edu/~bli/SBR16/project.html.

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  1. http://orca.st.usm.edu/~bli/SBR16/project.html

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Acknowledgments

This work is supported by Army Research Office grant W911NF-12-1-0057 to Dr. Yijuan Lu and Dr. Qi Tian, by NSF CRI-1305302, NSF CNS-1358939 and NSF OCI-1062439 to Dr. Yijuan Lu, and by the University of Southern Mississippi Faculty Startup Funds Award to Dr. Bo Li. We gratefully acknowledge the support from NVIDIA Corporation for the donation of the Titan X/Xp GPUs used in this research.

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Li, B., Yuan, J., Ye, Y. et al. 3D sketching for 3D object retrieval. Multimed Tools Appl 80, 9569–9595 (2021). https://doi.org/10.1007/s11042-020-10033-1

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