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Xu K.,National University of Defense Technology | Xu K.,Shenzhen VisuCA Key Laboratory SIAT | Zhang H.,Simon Fraser University | Jiang W.,National University of Defense Technology | And 4 more authors.
ACM Transactions on Graphics | Year: 2012

We present an algorithm for multi-scale partial intrinsic symmetry detection over 2D and 3D shapes, where the scale of a symmetric region is defined by intrinsic distances between symmetric points over the region. To identify prominent symmetric regions which overlap and vary in form and scale, we decouple scale extraction and symmetry extraction by performing two levels of clustering. First, significant symmetry scales are identified by clustering sample point pairs from an input shape. Since different point pairs can share a common point, shape regions covered by points in different scale clusters can overlap. We introduce the symmetry scale matrix (SSM), where each entry estimates the likelihood two point pairs belong to symmetries at the same scale. The pair-to-pair symmetry affinity is computed based on a pair signature which encodes scales. We perform spectral clustering using the SSM to obtain the scale clusters. Then for all points belonging to the same scale cluster, we perform the second-level spectral clustering, based on a novel point-to-point symmetry affinity measure, to extract partial symmetries at that scale. We demonstrate our algorithm on complex shapes possessing rich symmetries at multiple scales. © 2012 ACM.


Wang Y.,Shenzhen VisuCA Key Laboratory SIAT | Asafi S.,Tel Aviv University | Van Kaick O.,Simon Fraser University | Zhang H.,Simon Fraser University | And 2 more authors.
ACM Transactions on Graphics | Year: 2012

Unsupervised co-analysis of a set of shapes is a difficult problem since the geometry of the shapes alone cannot always fully describe the semantics of the shape parts. In this paper, we propose a semi-supervised learning method where the user actively assists in the co-analysis by iteratively providing inputs that progressively constrain the system. We introduce a novel constrained clustering method based on a spring system which embeds elements to better respect their inter-distances in feature space together with the usergiven set of constraints. We also present an active learning method that suggests to the user where his input is likely to be the most effective in refining the results. We show that each single pair of constraints affects many relations across the set. Thus, the method requires only a sparse set of constraints to quickly converge toward a consistent and error-free semantic labeling of the set. © 2012 ACM.


Xu K.,Shenzhen VisuCA Key Laboratory SIAT | Xu K.,National University of Defense Technology | Zhang H.,Simon Fraser University | Cohen-Or D.,Tel Aviv University | Chen B.,Shenzhen VisuCA Key Laboratory SIAT
ACM Transactions on Graphics | Year: 2012

We introduce set evolution as a means for creative 3D shape modeling, where an initial population of 3D models is evolved to produce generations of novel shapes. Part of the evolving set is presented to a user as a shape gallery to offer modeling suggestions. User preferences define the fitness for the evolution so that over time, the shape population will mainly consist of individuals with good fitness. However, to inspire the user's creativity, we must also keep the evolving set diverse. Hence the evolution is "fit and diverse", drawing motivation from evolution theory. We introduce a novel part crossover operator which works at the finer-level part structures of the shapes, leading to significant variations and thus increased diversity in the evolved shape structures. Diversity is also achieved by explicitly compromising the fitness scores on a portion of the evolving population. We demonstrate the effectiveness of set evolution on man-made shapes. We show that selecting only models with high fitness leads to an elite population with low diversity. By keeping the population fit and diverse, the evolution can generate inspiring, and sometimes unexpected, shapes. © 2012 ACM 0730-0301/2012/08-ART57.


Huang H.,Shenzhen VisuCA Key Laboratory SIAT | Huang H.,Simon Fraser University | Wu S.,Shenzhen VisuCA Key Laboratory SIAT | Wu S.,South China University of Technology | And 6 more authors.
ACM Transactions on Graphics | Year: 2013

We introduce L1-medial skeleton as a curve skeleton representation for 3D point cloud data. The L1-median is well-known as a robust global center of an arbitrary set of points. We make the key observation that adapting L1-medians locally to a point set representing a 3D shape gives rise to a one-dimensional structure, which can be seen as a localized center of the shape. The primary advantage of our approach is that it does not place strong requirements on the quality of the input point cloud nor on the geometry or topology of the captured shape. We develop a L 1-medial skeleton construction algorithm, which can be directly applied to an unoriented raw point scan with significant noise, outliers, and large areas of missing data. We demonstrate L1-medial skeletons extracted from raw scans of a variety of shapes, including those modeling high-genus 3D objects, plant-like structures, and curve networks. Copyright © ACM 2013.


Huang H.,Shenzhen VisuCA Key Laboratory SIAT | Huang H.,University of British Columbia | Yin K.,Shenzhen VisuCA Key Laboratory SIAT | Gong M.,Memorial University of Newfoundland | And 6 more authors.
ACM Transactions on Graphics | Year: 2013

Concocting a plausible composition from several non-overlapping image pieces, whose relative positions are not fixed in advance and without having the benefit of priors, can be a daunting task. Here we propose such a method, starting with a set of sloppily pasted image pieces with gaps between them. We first extract salient curves that approach the gaps from non-tangential directions, and use likely correspondences between pairs of such curves to guide a novel tele-registration method that simultaneously aligns all the pieces together. A structure-driven image completion technique is then proposed to fill the gaps, allowing the subsequent employment of standard in-painting tools to finish the job.


Zhang H.,Simon Fraser University | Xu K.,Shenzhen VisuCA Key Laboratory SIAT | Xu K.,HPCL Inc | Jiang W.,HPCL Inc | And 3 more authors.
ACM Transactions on Graphics | Year: 2013

We present an algorithm for hierarchical and layered analysis of irregular facades, seeking a high-level understanding of facade structures. By introducing layering into the analysis, we no longer view a facade as a flat structure, but allow it to be structurally separated into depth layers, enabling more compact and natural interpretations of building facades. Computationally, we perform a symmetry-driven search for an optimal hierarchical decomposition defined by split and layering operations applied to an input facade. The objective is symmetry maximization, i.e., to maximize the sum of symmetry of the substructures resulting from recursive decomposition. To this end, we propose a novel integral symmetry measure, which behaves well at both ends of the symmetry spectrum by accounting for all partial symmetries in a discrete structure. Our analysis results in a structural representation, which can be utilized for structural editing and exploration of building facades. Copyright © ACM. Copyright © ACM 2013.


Nan L.,Shenzhen VisuCA Key Laboratory SIAT | Xie K.,Shenzhen VisuCA Key Laboratory SIAT | Sharf A.,Ben - Gurion University of the Negev
ACM Transactions on Graphics | Year: 2012

We present an algorithm for recognition and reconstruction of scanned 3D indoor scenes. 3D indoor reconstruction is particularly challenging due to object interferences, occlusions and overlapping which yield incomplete yet very complex scene arrangements. Since it is hard to assemble scanned segments into complete models, traditional methods for object recognition and reconstruction would be inefficient. We present a search-classify approach which interleaves segmentation and classification in an iterative manner. Using a robust classifier we traverse the scene and gradually propagate classification information. We reinforce classification by a template fitting step which yields a scene reconstruction. We deform-to-fit templates to classified objects to resolve classification ambiguities. The resulting reconstruction is an approximation which captures the general scene arrangement. Our results demonstrate successful classification and reconstruction of cluttered indoor scenes, captured in just few minutes. © 2012 ACM.


Xu K.,Shenzhen VisuCA Key Laboratory SIAT | Xu K.,National Defense University | Ma R.,Simon Fraser University | Zhang H.,Simon Fraser University | And 4 more authors.
ACM Transactions on Graphics | Year: 2014

We introduce focal points for characterizing, comparing, and organizing collections of complex and heterogeneous data and apply the concepts and algorithms developed to collections of 3D indoor scenes. We represent each scene by a graph of its constituent objects and define focal points as representative substructures in a scene collection. To organize a heterogeneous scene collection, we cluster the scenes based on a set of extracted focal points: scenes in a cluster are closely connected when viewed from the perspective of the representative focal points of that cluster. The key concept of representativity requires that the focal points occur frequently in the cluster and that they result in a compact cluster. Hence, the problem of focal point extraction is intermixed with the problem of clustering groups of scenes based on their representative focal points. We present a co-analysis algorithm which interleaves frequent pattern mining and subspace clustering to extract a set of contextual focal points which guide the clustering of the scene collection. We demonstrate advantages of focal-centric scene comparison and organization over existing approaches, particularly in dealing with hybrid scenes, scenes consisting of elements which suggest membership in different semantic categories. Copyright © ACM.


Wu Z.,Zhejiang University | Shou R.,Zhejiang University | Wang Y.,Shenzhen VisuCA Key Laboratory SIAT | Liu X.,Zhejiang University
Computers and Graphics (Pergamon) | Year: 2014

In this paper, we present an interactive approach for shape co-segmentation via label propagation. Our intuitive approach is able to produce error-free results and is very effective at handling out-of-sample data. Specifically, we start by over-segmenting a set of shapes into primitive patches. Then, we allow the users to assign labels to some patches and propagate the label information from these patches to the unlabeled ones. We iterate the last two steps until the error-free consistent segmentations are obtained. Additionally, we provide an inductive extension of our framework, which effectively addresses the out-of-sample data. The experimental results demonstrate the effectiveness of our approach. © 2013 Elsevier Ltd. All rights reserved.


Wu Z.,Zhejiang University | Wang Y.,Shenzhen VisuCA Key Laboratory SIAT | Shou R.,Zhejiang University | Chen B.,Shenzhen VisuCA Key Laboratory SIAT | Liu X.,Zhejiang University
Computers and Graphics (Pergamon) | Year: 2013

Many shape co-segmentation methods employ multiple descriptors to measure the similarities between parts of a set of shapes in a descriptor space. Different shape descriptors characterize a shape in different aspects. Simply concatenating them into a single vector might greatly degrade the performance of the co-analysis in the presence of irrelevant and redundant information. In this paper, we propose an approach to fuse multiple descriptors for unsupervised co-segmentation of a set of shapes from the same family. Starting from the over-segmentations of shapes, our approach generates the consistent segmentation by performing the spectral clustering in a fused space of shape descriptors. The core of our approach is to seek for an optimal combination of affinity matrices of different descriptors so as to alleviate the impact of unreliable and irrelevant features. More specially, we introduce a local similarity based affinity aggregation spectral clustering algorithm, which assumes the local similarities are more reliable than far-away ones. Experimental results show the efficiency of our approach and improvements over the state-of-the-art algorithms on the benchmark datasets. © 2013 Published by Elsevier Ltd. All rights reserved.

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