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Relating Shapes Via Geometric Symmetries and Regularities Relating Shapes via Geometric Symmetries and Regularities Art Tevs Qixing Huang Michael Wand Hans-Peter Seidel Leonidas Guibas MPI Informatics Stanford University Utrecht University MPI Informatics Stanford University Stanford University Figure 1: Chandeliers data set with matched lamps over several geometrically varying configurations. Abstract A core building block for this task is the ability to establish corre- spondences In this paper we address the problem of finding correspondences [van Kaick et al. 2011]: We need to be able to match between related shapes of widely varying geometry. We propose a objects, and parts of them according to multiple notions of simi- new method based on the observation that symmetry and regularity larity. In other words, correspondence computation means the re- in shapes is often associated with their function. Hence, they pro- covery of a latent equivalence relation that identifies similar pieces vide cues for matching related geometry even under strong shape of shapes. The equivalence classes unveil redundancy in a model variations. Correspondingly, we decomposes shapes into overlap- collection, thereby providing a basic structuring tool. ping regions determined by their regularity properties. Afterwards, As detailed in Section 2, a large body of techniques is available we form a graph that connects these pieces via pairwise relations for estimating correspondences that involve simple transformations that capture geometric relations between rotation axes and reflec- with few degrees of freedom, such as rigid motions or affine maps. tion planes as well as topological or proximity relations. Finally, we More recently, this also comprises intrinsic isometries, which per- perform graph matching to establish correspondences. The method mit matching of objects in varying poses as long as there is no sub- yields certain more abstract but semantically meaningful correspon- stantial intrinsic stretch. dences between man-made shapes that are too difficult to recognize by traditional geometric methods. However, matching semantically related shapes with considerable variation in geometry remains a very difficult problem. Objects CR Categories: I.3.5 [Computing Methodologies]: Computer such as humans in different poses can be matched by smooth Graphics—Computational Geometry and Object Modeling; deformation functions using numerical optimization [Allen et al. 2003], typically requiring manual initialization. Relating collec- Keywords: shape correspondences, symmetry, structural regular- tions of shapes without manual intervention has become feasible if ity, shape understanding the collection forms a dense enough sampling of a smooth shape space [Ovsjanikov et al. 2011; Huang et al. 2012; Kim et al. 2012]. Links: DL PDF However, these assumptions are not always met. In particular, man- made artifacts of related functionality can have very different shape 1 Introduction with non-continuous variability. One of the big challenges in modern computer graphics is to or- We propose a new technique for relating shapes of similar function- ganize, structure, and to some extent “understand” visual data and ality but potentially very different geometry based on shared sym- 3D geometry. We now have large data bases of shape collections, metry properties, complementary to extant approaches. We build public ones such as Trimble 3D WarehouseTM, as well as other aca- upon the observation that symmetry and regularity (we will use demic, commercial, and possibly in-house collections. These pro- these terms interchangeably) are often related to functionality: For vide numerous 3D assets in varying composition, style, and level- example, a windmill is rotationally symmetric but not reflectively of-detail. because it must be driven by wind. The same holds for screws and propellers. However, a windmill is different from a plane or a sub- Being able to explore, structure, and utilize the content in such data marine in that the propeller is attached to a typically rotationally remains one of the key challenges in computer graphics research. symmetric building, joining the rotation axes in a roughly perpen- dicular angle, unlike the other two. The idea of our paper is to systematically extract such regularity properties and pairwise rela- ACM Reference Format tions between their invariant sets (rotation axes, reflection planes) Tevs, A., Huang, Q., Wand, M., Seidel, H., Guibas, L. 2014. Relating Shapes via Geometric Symmetries and in order to characterize shape families at a high level of abstraction. Regularities. ACM Trans. Graph. 33, 4, Article 119 (July 2014), 12 pages. DOI = 10.1145/2601097.2601220 http://doi.acm.org/10.1145/2601097.2601220. We detect regularity in the form of Euclidean symmetry groups act- Copyright Notice Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted ing on the shape geometry. We describe the symmetry structure without fee provided that copies are not made or distributed for profi t or commercial advantage and that copies bear this notice and the full citation on the fi rst page. Copyrights for components of this work owned of shapes by a pairwise structure graphs on pieces of geometry. by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or re- The nodes are symmetries and edges are pairwise relations between publish, to post on servers or to redistribute to lists, requires prior specifi c permission and/or a fee. Request permissions from [email protected]. symmetries, such as angles or topological relations, such as set in- 2014 Copyright held by the Owner/Author. Publication rights licensed to ACM. 0730-0301/14/07-ART119 $15.00. clusion and adjacency. Afterwards, we employ graph matching to DOI: http://dx.doi.org/10.1145/2601097.2601220 find correspondences between shapes in this more abstract and in- ACM Transactions on Graphics, Vol. 33, No. 4, Article 119, Publication Date: July 2014 119:2 • A. Tevs et al. (a) Cn (b) Cnv (c) S2n (d) Dnh (e) Oh(cube) (f) L2 with D4h single rotation axis dihedral platonic solid lattice with compatible point symmetry groups point group Figure 2: Examples of Euclidean symmetry groups. Point-symmetries combine rotations and reflections and differ in the number of rotation axes and reflection planes, and their mutual relation. The remaining cases combine infinite translational lattices with optional point groups. variant representation. Our method is complementary to traditional been employed by Cullen et al. [2011] for relating shapes and cre- geometric methods. It does not require training data or collections ating shape variations, as well as by Simary et al. [2009] for shape to co-analyze but can directly match pairs of shapes. segmentation; the method incorporates relations such as perpendic- ularity of symmetry planes between adjacent segments. Our approach is essentially a second-order shape comparator, com- paring shapes not by traditional geometry-matching between the Our method is different in two important aspects: First, we aim at shapes, but by comparing the geometry matches (symmetries) correspondence estimation and develop the necessary tools (struc- within each shape to each other. Such symmetries and self-relations ture model, graph decomposition, comparison functions) for this capture aspects of the shape that are important both to its func- task. Second, our technical approach is different: Hierarchies are tion and to our perceptual understanding of it. While earlier work usually not unique, requiring non-canonical choices. We therefore has addressed the estimation of such shape self-structure, this pa- build graphs that reflect algebraic symmetry properties and topolog- per provides a canonical way of encoding these relations in a graph ical aspects of the shape. This does include relations of hierarchi- and for comparing such symmetry graphs even when the symme- cal containment. However, our representation does not compress tries present are not exactly the same, effectively exploiting a novel detected hierarchies. This maintains a richer set of structural re- metric structure in the space of symmetries. lations, making it better suited for correspondence problems. Van Kaik et al. [2013] obtain information from optimizing hierarchies for shape collections; our paper restricts itself to pairwise match- 2 Related Work ing, where this is not possible. Graph-based matching has previ- ously been studied by Fisher et al. [2011] with restriction to local Correspondence in 3D geometry has received a lot of attention in spatial proximity relations while we use non-local invariants. Liu et recent years; van Kaik et al. [2011] provide a good survey. There al. [2012] also use symmetries to aid correspondence computation; are mature solutions for matching geometry under the action of a (single) dominant intrinsic involution is computed [Xu et al. 2009] groups of transformations with few degrees of freedom such as rigid as reference for dense correspondences; our method obtains coarse matching. Matching objects that are semantically related but have correspondences from a large set of different extrinsic symmetries. widely varying geometry is very much an open problem; only a few specialized solutions exist. One possibility is to use machine Symmetry groups have been used for matching in the image domain learning to associate semantic categories with geometry [Kaloger- by [Hauagge and Snavely 2012; Henderson et al. 2012], but the ap- akis et al. 2010]. This
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