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28
Acquiring 3D Indoor Environments with Variability and Repetition
 ACM Transactions on Graphics (TOG
"... input singleview scan recognized objects retrieved and posed models office scene Figure 1: (Left) Given a single view scan of a 3D environment obtained using a fast range scanner, we perform scene understanding by recognizing repeated objects, while factoring out their modes of variability (middle) ..."
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Cited by 41 (7 self)
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input singleview scan recognized objects retrieved and posed models office scene Figure 1: (Left) Given a single view scan of a 3D environment obtained using a fast range scanner, we perform scene understanding by recognizing repeated objects, while factoring out their modes of variability (middle). The repeating objects have been learned beforehand as low complexity models, along with their joint deformations. We extract the objects despite a poor quality input scan with large missing parts and many outliers. The extracted parameters can then be used to pose 3D models to create a plausible scene reconstruction (right). Largescale acquisition of exterior urban environments is by now a wellestablished technology, supporting many applications in search, navigation, and commerce. The same is, however, not the case for indoor environments, where access is often restricted and the spaces are cluttered. Further, such environments typically contain a high density of repeated objects (e.g., tables, chairs, monitors, etc.) in regular or nonregular arrangements with significant pose variations and articulations. In this paper, we exploit the special structure of indoor environments to accelerate their 3D acquisition and recognition with a lowend handheld scanner. Our approach runs in two phases: (i) a learning phase wherein we acquire 3D models of frequently occurring objects and capture their variability modes from only a few scans, and (ii) a recognition phase wherein from a single scan of a new area, we identify previously seen objects but in different poses and locations at an average recognition time of 200ms/model. We evaluate the robustness and limits of the proposed recognition system using a range of synthetic and real world scans under challenging settings.
StructureAware Shape Processing
 EUROGRAPHICS ’13 / MATEU SBERT AND LÁSZLÓ SZIRMAYKALOS
, 2013
"... Shape structure is about the arrangement and relations between shape parts. Structureaware shape processing goes beyond local geometry and low level processing, and analyzes and processes shapes at a high level. It focuses more on the global inter and intra semantic relations among the parts of sha ..."
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Cited by 22 (9 self)
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Shape structure is about the arrangement and relations between shape parts. Structureaware shape processing goes beyond local geometry and low level processing, and analyzes and processes shapes at a high level. It focuses more on the global inter and intra semantic relations among the parts of shape rather than on their local geometry. With recent developments in easy shape acquisition, access to vast repositories of 3D models, and simpletouse desktop fabrication possibilities, the study of structure in shapes has become a central research topic in shape analysis, editing, and modeling. A whole new line of structureaware shape processing algorithms has emerged that base their operation on an attempt to understand such structure in shapes. The algorithms broadly consist of two key phases: an analysis phase, which extracts structural information from input data; and a (smart) processing phase, which utilizes the extracted information for exploration, editing, and synthesis of novel shapes. In this survey paper, we organize, summarize, and present the key concepts and methodological approaches towards efficient structureaware shape processing. We discuss common models of structure, their implementation in terms of mathematical formalism and algorithms, and explain the key principles in the context of a number of stateoftheart approaches. Further, we attempt to list the key open problems and challenges, both at the technical and at the conceptual level, to make it easier for new researchers to better explore and contribute to this topic. Our goal is to both give the practitioner an overview of available structureaware shape processing techniques, as well as identify future research questions in this important, emerging, and fascinating research area.
Layered Analysis of Irregular Facades via Symmetry Maximization
"... Figure 1: Symmetrydriven structural analysis of an irregular facade (a) results in a hierarchical decomposition (b) into regular grids. Our analysis introduces layering (b), going beyond flat segmentation via splits (c) and allowing more compact and natural structural representations. The resulting ..."
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Cited by 10 (1 self)
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Figure 1: Symmetrydriven structural analysis of an irregular facade (a) results in a hierarchical decomposition (b) into regular grids. Our analysis introduces layering (b), going beyond flat segmentation via splits (c) and allowing more compact and natural structural representations. The resulting hierarchical model of facades enables applications such as structural editing (d) and retargeting (e). We present an algorithm for hierarchical and layered analysis of irregular facades, seeking a highlevel 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 symmetrydriven 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. Links: DL PDF WEB VIDEO DATA CODE 1
MultiScale Partial Intrinsic Symmetry Detection
"... shown in uniform color. Note the detection of inter and intraobject symmetries, as well as cylindrical symmetry of the limbs. We present an algorithm for multiscale partial intrinsic symmetry detection over 2D and 3D shapes, where the scale of a symmetric region is defined by intrinsic distances ..."
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Cited by 10 (4 self)
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shown in uniform color. Note the detection of inter and intraobject symmetries, as well as cylindrical symmetry of the limbs. We present an algorithm for multiscale 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 pairtopair 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 secondlevel spectral clustering, based on a novel pointtopoint symmetry affinity measure, to extract partial symmetries at that scale. We demonstrate our algorithm on complex shapes possessing rich symmetries at multiple scales. Links: DL PDF WEB DATA 1
Shape2Pose: HumanCentric Shape Analysis
"... As 3D acquisition devices and modeling tools become widely available there is a growing need for automatic algorithms that analyze the semantics and functionality of digitized shapes. Most recent research has focused on analyzing geometric structures of shapes. Our work is motivated by the observat ..."
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Cited by 9 (3 self)
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As 3D acquisition devices and modeling tools become widely available there is a growing need for automatic algorithms that analyze the semantics and functionality of digitized shapes. Most recent research has focused on analyzing geometric structures of shapes. Our work is motivated by the observation that a majority of manmade shapes are designed to be used by people. Thus, in order to fully understand their semantics, one needs to answer a fundamental question: “how do people interact with these objects? ” As an initial step towards this goal, we offer a novel algorithm for automatically predicting a static pose that a person would need to adopt in order to use an object. Specifically, given an input 3D shape, the goal of our analysis is to predict a corresponding human pose, including contact points and kinematic parameters. This is especially challenging for manmade objects that commonly exhibit a lot of variance in their geometric structure. We address this challenge by observing that contact points usually share consistent local geometric features related to the anthropometric properties of corresponding parts and that human body is subject to kinematic constraints and priors. Accordingly, our method effectively combines local region classification and global kinematicallyconstrained search to successfully predict poses for various objects. We also evaluate our algorithm on six diverse collections of 3D polygonal models (chairs, gym equipment, cockpits, carts, bicycles, and bipedal devices) containing a total of 147 models. Finally, we demonstrate that the poses predicted by our algorithm can be used in several shape analysis problems, such as establishing correspondences between objects, detecting salient regions, finding informative viewpoints, and retrieving functionallysimilar shapes.
Dual Strip Weaving: Interactive Design of Quad Layouts using Elastica Strips
"... Figure 1: Overview of our Dual Strip Weaving approach for the design of quadrilateral patch layouts. a) When hovering over the object, the user is immediately presented with the best elastica strip (visualized using a stripe pattern) at the current pointer position. It can be selected and fixed with ..."
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Cited by 5 (2 self)
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Figure 1: Overview of our Dual Strip Weaving approach for the design of quadrilateral patch layouts. a) When hovering over the object, the user is immediately presented with the best elastica strip (visualized using a stripe pattern) at the current pointer position. It can be selected and fixed with a single click. b) Fixed strips (blue) constrain the design space; only compatible strips are offered next (green). c) Indicators based on colorcoding and stripe patterns guide the user to regions where modifications are recommended for the benefit of layout quality. d) Finally, the implied quad layout structure is derived from a collection of strips. The accompanying video shows the entire process. a) b) c) d) We introduce Dual Strip Weaving, a novel concept for the interactive design of quad layouts, i.e. partitionings of freeform surfaces into quadrilateral patch networks. In contrast to established tools for the design of quad layouts or subdivision base meshes, which are often based on creating individual vertices, edges, and quads, our method takes a more global perspective, operating on a higher level of abstraction: the atomic operation of our method is the creation of an entire cyclic strip, delineating a large number of quad patches at once. The global consistencypreserving nature of this approach reduces demands on the user’s expertise by requiring less advance planning. Efficiency is achieved using a novel method at the heart of our system, which automatically proposes geometrically and topologically suitable strips to the user. Based on this we provide interaction tools to influence the design process to any desired degree and visual guides to support the user in this task.
Detecting Symmetry in Scalar Fields Using Augmented Extremum Graphs
"... Fig. 1. Robust scalar field symmetry identification algorithm detects symmetry even in the presence of significant noise in the electron microscopy data of the Rubisco RbcL8RbcX28 complex (EMDB 1654). (left) Volume rendering shows symmetry and noise in the data. (center) A set of seed cells is cho ..."
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Cited by 4 (1 self)
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Fig. 1. Robust scalar field symmetry identification algorithm detects symmetry even in the presence of significant noise in the electron microscopy data of the Rubisco RbcL8RbcX28 complex (EMDB 1654). (left) Volume rendering shows symmetry and noise in the data. (center) A set of seed cells is chosen as source vertices for traversing the augmented extremum graph of the data. During the traversal, the seed cells merge together to form four symmetric superseeds. Seed cells that belong to a common superseed are shown with the same color. (right) The initial estimate of symmetry is expanded in a region growing stage to identify the symmetric regions. A symmetryaware transfer function highlights the 4fold rotational symmetry detected in the Rubisco complex. Abstract—Visualizing symmetric patterns in the data often helps the domain scientists make important observations and gain insights about the underlying experiment. Detecting symmetry in scalar fields is a nascent area of research and existing methods that detect symmetry are either not robust in the presence of noise or computationally costly. We propose a data structure called the augmented extremum graph and use it to design a novel symmetry detection method based on robust estimation of distances. The augmented extremum graph captures both topological and geometric information of the scalar field and enables robust and computationally efficient detection of symmetry. We apply the proposed method to detect symmetries in cryoelectron microscopy datasets and the experiments demonstrate that the algorithm is capable of detecting symmetry even in the presence of significant noise. We describe novel applications that use the detected symmetry to enhance visualization of scalar field data and facilitate their exploration. Index Terms—Scalar field visualization, extremum graph, Morse decomposition, symmetry detection, data exploration. 1
Nearregular structure discovery using linear programming
 ACM Trans. Graph
"... Nearregular structures are common in manmade and natural objects. Algorithmic detection of such regularity greatly facilitates our understanding of shape structures, leads to compact encoding of input geometries, and enables efficient generation and manipulation of complex patterns on both acqui ..."
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Cited by 3 (1 self)
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Nearregular structures are common in manmade and natural objects. Algorithmic detection of such regularity greatly facilitates our understanding of shape structures, leads to compact encoding of input geometries, and enables efficient generation and manipulation of complex patterns on both acquired and synthesized objects. Such regularity manifests itself both in the repetition of certain geometric elements, as well as in the structured arrangement of the elements. We cast the regularity detection problem as an optimization and efficiently solve it using linear programming techniques. Our optimization has a discrete aspect, i.e., the connectivity relationships among the elements; as well as a continuous aspect, i.e., the locations of the elements of interest. Both these aspects are captured by our nearregular structure extraction framework, which alternates between discrete and continuous optimizations. We demonstrate the effectiveness of our framework on a variety of problems including nearregular structure extraction, structurepreserving pattern manipulation, and markerless correspondence detection. Robustness results with respect to geometric and topological noise are presented on synthesized, realworld, and also benchmark datasets.
Multiscale Symmetry Detection in Scalar Fields by Clustering Contours
"... Fig. 1. Clustering based analysis detects symmetry at different scales in a 3D cryoelectron microscopy image of AMPactivated kinase (EMDB1897). (left) The threefold rotational symmetry is apparent from the volume rendering. (center) Contours are represented as points in a highdimensional shape ..."
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Cited by 2 (0 self)
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Fig. 1. Clustering based analysis detects symmetry at different scales in a 3D cryoelectron microscopy image of AMPactivated kinase (EMDB1897). (left) The threefold rotational symmetry is apparent from the volume rendering. (center) Contours are represented as points in a highdimensional shape descriptor space (illustrated in 2D). Symmetric contours form a cluster in the descriptor space and can be easily identified. Three such clusters are shown in gold, blue, and pink. (right) Three symmetric regions of different sizes, highlighted in gold, blue, and pink, detected by the method. Abstract—The complexity in visualizing volumetric data often limits the scope of direct exploration of scalar fields. Isocontour extraction is a popular method for exploring scalar fields because of its simplicity in presenting features in the data. In this paper, we present a novel representation of contours with the aim of studying the similarity relationship between the contours. The representation maps contours to points in a highdimensional transformationinvariant descriptor space. We leverage the power of this representation to design a clustering based algorithm for detecting symmetric regions in a scalar field. Symmetry detection is a challenging problem because it demands both segmentation of the data and identification of transformation invariant segments. While the former task can be addressed using topological analysis of scalar fields, the latter requires geometry based solutions. Our approach combines the two by utilizing the contour tree for segmenting the data and the descriptor space for determining transformation invariance. We discuss two applications, query driven exploration and asymmetry visualization, that demonstrate the effectiveness of the approach. Index Terms—Scalar field visualization, symmetry detection, contour tree, data exploration. 1
PatternDriven Colorization of 3D Surfaces
"... Colorization refers to the process of adding color to black & white images or videos. This paper extends the term to handle surfaces in three dimensions. This is important for applications in which the colors of an object need to be restored and no relevant image exists for texturing it. We focu ..."
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Cited by 1 (0 self)
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Colorization refers to the process of adding color to black & white images or videos. This paper extends the term to handle surfaces in three dimensions. This is important for applications in which the colors of an object need to be restored and no relevant image exists for texturing it. We focus on surfaces with patterns and propose a novel algorithm for adding colors to these surfaces. The user needs only to scribble a few color strokes on one instance of each pattern, and the system proceeds to automatically colorize the whole surface. For this scheme to work, we address not only the problem of colorization, but also the problem of pattern detection on surfaces. 1.