caa a22 4500 605541019 CHVBK 20210128100914.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s00371-015-1091-1 doi (NATIONALLICENCE)springer-10.1007/s00371-015-1091-1 Learning best views of 3D shapes from sketch contour [Elektronische Daten] [Long Zhao, Shuang Liang, Jinyuan Jia, Yichen Wei] In this paper, we introduce a novel learning-based approach to automatically select the best views of 3D shapes using a new prior. We think that a viewpoint of the 3D shape is reasonable if a human usually draws the shape from it. Hand-drawn sketches collected from relevant datasets are used to model this concept. We reveal the connection between sketches and viewpoints by taking context information of their contours into account. Furthermore, a learning framework is proposed to generalize this connection which aims to learn an automatic best view selector for different kinds of 3D shapes. Experiments on the Princeton Shape Benchmark dataset are conducted to demonstrate the superiority of our approach. The results show that compared with other state-of-the-art methods, our approach is not only robust but also efficient when applied to shape retrieval tasks. Springer-Verlag Berlin Heidelberg, 2015 Best view selection nationallicence Sketch-based modeling nationallicence Context similarity nationallicence Bag-of-features nationallicence Zhao Long Tongji University, Shanghai, China aut Liang Shuang Tongji University, Shanghai, China aut Jia Jinyuan Tongji University, Shanghai, China aut Wei Yichen Microsoft Research, Beijing, China aut The Visual Computer Springer Berlin Heidelberg 31/6-8(2015-06-01), 765-774 0178-2789 31:6-8<765 2015 31 371 https://doi.org/10.1007/s00371-015-1091-1 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00371-015-1091-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhao Long Tongji University, Shanghai, China aut NATIONALLICENCE 700 1- Liang Shuang Tongji University, Shanghai, China aut NATIONALLICENCE 700 1- Jia Jinyuan Tongji University, Shanghai, China aut NATIONALLICENCE 700 1- Wei Yichen Microsoft Research, Beijing, China aut NATIONALLICENCE 773 0- The Visual Computer Springer Berlin Heidelberg 31/6-8(2015-06-01), 765-774 0178-2789 31:6-8<765 2015 31 371