Sketch-based 3D Object Retrieval with Skeleton Line Views - Initial Results and Research Problems
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Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Hand-drawn sketches are a convenient way to define 3D object retrieval queries. Numerous methods have been proposed for sketch-based 3D object retrieval. Such methods employ a non-photo-realistic rendering step to create sketch-like views from 3D objects for comparison with the sketch queries. An implicit assumption here often is that the sketch query resembles a perspective view of the 3D shape. However, based on personal inclination or the type of object, users often tend to draw skeleton views instead of a perspective one. In those cases, a retrieval relying on perspective views is not the best choice, as features extracted from skeleton-based sketches and perspective can be expected to diverge vastly. In this paper, we report on our ongoing work to implement sketch-based 3D object retrieval for skeleton query sketches. Furthermore, we provide an initial benchmark data set consisting of skeleton sketches for a selection of generic object classes. Then, we design a sketch-based retrieval processing pipeline involving a sketch rendering step using Laplacian contraction. Additional experimental results indicate that skeleton sketches can be automatically distinguished from perspective sketches, and that the proposed method works for selected object classes. We also identify object classes for which the rendering of skeleton views is difficult, motivating further research.
Description
@inproceedings{10.2312:3dor.20171052,
booktitle = {Eurographics Workshop on 3D Object Retrieval},
editor = {Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov},
title = {{Sketch-based 3D Object Retrieval with Skeleton Line Views - Initial Results and Research Problems}},
author = {Zhao, Xueqing and Gregor, Robert and Mavridis, Pavlos and Schreck, Tobias},
year = {2017},
publisher = {The Eurographics Association},
ISSN = {1997-0471},
ISBN = {978-3-03868-030-7},
DOI = {10.2312/3dor.20171052}
}