Selected Publications (See my pages on Google Scholar Citations and DBLP)
2016
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Changqing Zou*, Junjie Cao*, W. Ranaweera, I. Alhashim, Ping Tan, Alla Sheffer, HaoZhang. Legible Compact Calligrams.
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2015
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Changqing Zou, Zhe Huang, Rynson W. H. Lau, Jianzhuang Liu, Hongbo Fu. Sketch-based Shape Retrieval using Pyramid-of Parts. CoRR abs/1502.04232 (2015).
We present a multi-scale approach to sketch-based shape retrieval. It is based on a novel multi-scale shape descriptor called Pyramidof-Parts, which encodes the features and spatial relationship of the semantic parts of query sketches. The same descriptor can also be used to represent 2D projected views of 3D shapes, allowing effective matching of query sketches with 3D shapes across multiple scales... [PD_F] |
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Changqing Zou, Shifeng Chen, Hongbo Fu, Jianzhuang Liu:
Progressive 3D Reconstruction of Planar-Faced Manifold Objects with DRF-Based Line Drawing Decomposition. IEEE Trans. Vis. Comput. Graph. 21(2): 252-263 (2015) This paper proposes an approach to reconstructing polyhedral objects from single-view line drawings. Given a complex line drawing representing a manifold object, our approach starts with the efficient decomposition of the line drawing into a series of simpler line drawings, by analyzing the degree of reconstruction freedom. A rough complete 3D object is then obtained by progressively reconstructing rough 3D shapes from such decomposed simpler line drawings. Finally we apply a 3D beautification algorithm to the rough complete 3D object to get perceptually more meaningful results... [pdf] |
2014
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Changqing Zou, Heng Yang, Jianzhuang Liu:
Separation of Line Drawings Based on Split Faces for 3D Object Reconstruction. CVPR 2014: 692-699 We propose an effective method to conduct the line drawing separation and turn a complex line drawing into parametric 3D models. This is achieved by recursively separating the line drawing using two types of split faces. Our experiments show that the proposed separation method can generate more basic and simple line drawings, and its combination with the example-based reconstruction can robustly recover wider range of complex parametric 3D objects than previous methods. [pdf] |
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Changqing Zou, Xiaojiang Peng, Hao Lv, Shifeng Chen, Hongbo Fu, Jianzhuang Liu: Sketch-based 3-D modeling for piecewise planar objects in single images. Computers & Graphics 46: 130-137 (2014).
3-D object modeling from single images has many applications in computer graphics and multimedia. Most previous 3-D modeling methods which directly recover 3-D geometry from single images require user interactions during the whole modeling process. In this paper, we propose a semi-automatic 3-D modeling approach to recover accurate 3-D geometry from a single image of a piecewise planar object with less user interaction. Our approach concentrates on these three aspects: (1) requiring rough sketch input only, (2) accurate modeling for a large class of objects, and (3) automatically recovering the invisible part of an object and providing a complete 3-D model. Experimental results on various objects show that the proposed approach provides a good solution to these three problems. [pdf] |
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Changqing Zou, Changhu Wang, Yafei Wen, Lei Zhang, Jianzhuang Liu: Viewpoint-Aware Representation for Sketch-Based 3D Model Retrieval. IEEE Signal Process. Lett. 21(8): 966-970 (2014).
We study the problem of sketch-based 3D model retrieval, and propose a solution powered by a new query-to-model distance metric and a powerful feature descriptor based on the bag-of-features framework... [pdf]_ |
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Xiaojiang Peng, Changqing Zou, Yu Qiao, Qiang Peng:
Action Recognition with Stacked Fisher Vectors. ECCV (5) 2014: 581-595. Representation of video is a vital problem in action recognition. This paper proposes Stacked Fisher Vectors (SFV), a new representation with multi-layer nested Fisher vector encoding, for action recognition. In the first layer, we densely sample large subvolumes from input videos, extract local features, and encode them using Fisher vectors (FVs). The second layer compresses the FVs of subvolumes obtained in previous layer, and then encodes them again with Fisher vectors... [pdf] |