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Semantic Similarity Metric Learning for Sketch-Based 3D Shape Retrieval.

Xia, Y., Wang, S., You, L. and Zhang, J. J., 2021. Semantic Similarity Metric Learning for Sketch-Based 3D Shape Retrieval. In: ICCS 2021: International Conference on Computational Science, 16-18 June 2021, Krakow, Poland, 59 - 69.

Full text available as:

ICCS_2021_paper_149.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1007/978-3-030-77977-1_5


Since the development of the touch screen technology makes sketches simple to draw and obtain, sketch-based 3D shape retrieval has received increasing attention in the community of computer vision and graphics in recent years. The main challenge is the big domain discrepancy between 2D sketches and 3D shapes. Most existing works tried to simultaneously map sketches and 3D shapes into a joint feature embedding space, which has a low efficiency and high computational cost. In this paper, we propose a novel semantic similarity metric learning method based on a teacher-student strategy for sketch-based 3D shape retrieval. We first extract the pre-learned semantic features of 3D shapes from the teacher network and then use them to guide the feature learning of 2D sketches in the student network. The experiment results show that our method has a better retrieval performance.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Sketch · 3D shape · Retrieval · Metric learning · Semantic feature
Group:Faculty of Media & Communication
ID Code:35894
Deposited By: Symplectic RT2
Deposited On:11 Aug 2021 09:36
Last Modified:14 Mar 2022 14:29


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