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DisCo

We present DisCo, a novel display-camera communication system. DisCo enables displays and cameras to communicate with each other while also displaying and capturing images for human consumption. Messages are transmitted by temporally modulating the display brightness at high frequencies so that they are imperceptible to humans. Messages are... (more)

Example-Based Sketch Segmentation and Labeling Using CRFs

We introduce a new approach for segmentation and label transfer in sketches that substantially improves the state of the art. We build on successful... (more)

Interactive High-Quality Green-Screen Keying via Color Unmixing

Due to the widespread use of compositing in contemporary feature films, green-screen keying has become an essential part of postproduction workflows.... (more)

Image Perforation

Image pipelines arise frequently in modern computational photography systems and consist of multiple processing stages where each stage produces an intermediate image that serves as input to a future stage. Inspired by recent work on loop perforation [Sidiroglou-Douskos et al. 2011], this article introduces image perforation, a new... (more)

Printed Perforated Lampshades for Continuous Projective Images

We present a technique for designing three-dimensional- (3D) printed perforated lampshades that project continuous grayscale images onto the... (more)

A Probabilistic Model for Exteriors of Residential Buildings

We propose a new framework to model the exterior of residential buildings. The main goal of our work is to design a model that can be learned from... (more)

Nonuniform Spatial Deformation of Light Fields by Locally Linear Transformations

Light-field cameras offer new imaging possibilities compared to conventional digital cameras. However, the additional angular domain of light fields... (more)

Hexahedral-Dominant Meshing

This article introduces a method that generates a hexahedral-dominant mesh from an input tetrahedral mesh. It follows a three-step pipeline similar to the one proposed by Carrier Baudoin et al.: (1) generate a frame field, (2) generate a pointset P that is mostly organized on a regular grid locally aligned with the frame field, and (3) generate the... (more)

Efficient and Flexible Deformation Representation for Data-Driven Surface Modeling

Effectively characterizing the behavior of deformable objects has wide applicability but remains challenging. We present a new rotation-invariant... (more)

A Non-Parametric Factor Microfacet Model for Isotropic BRDFs

We investigate the expressiveness of the microfacet model for isotropic bidirectional reflectance distribution functions (BRDFs) measured from real... (more)

Integration with Stochastic Point Processes

We present a novel comprehensive approach for studying error in integral estimation with point distributions based on point process statistics. We derive exact formulae for bias and variance of integral estimates in terms of the spatial or spectral characteristics of integrands and first- and-second order product density measures of general point... (more)

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About TOG

ACM TOG is the foremost peer-reviewed journal in the area of computer graphics. 

Recent impact factor calculations from Thomson Reuters give ACM TOG an impact factor of 4.096 and an Eigenfactor Score of 0.029, giving it the top ranking among the 104 journals in the Computer Science: Software Engineering category. 

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Forthcoming Articles
A Compressed Representation for Ray Tracing Parametric Surfaces

Parametric surfaces are an essential modeling tool in computer aided design and movie production. Even though their use is well established in industry, generating ray-traced images adds significant cost in time and memory consumption. Ray tracing such surfaces is usually accomplished by subdividing the surfaces on-the-fly, or by conversion to polygonal representations. However, on-the-fly subdivision is computationally very expensive, whereas polygonal meshes require large amounts of memory. This is a particular problem for parametric surfaces with displacement, where very fine tessellation is required to faithfully represent the shape. Hence, memory restrictions are the major challenge in production rendering. In this paper, we present a novel solution to this problem. We propose a compression scheme for BVHs on parametric patches, that significantly reduces data requirements for hierarchies. We further propose an approximate evaluation method that does not require leaf geometry, yielding an overall reduction of memory consumption by a factor of 60 over regular BVHs on indexed face sets and by a factor of 16 over established state-of-the-art compression schemes. Alternatively, our compression can simply be applied to a standard BVH while keeping the leaf geometry, resulting in a compression rate of up to 2:1 over current methods.

Azimuthal Scattering from Elliptical Hair Fibers

The appearance of hair follows from the small-scale geometry of hair fibers, with the cross-sectional shape determining the azimuthal distribution of scattered light. Although previous research has described some of the effects of non-circular cross sections, no accurate scattering models for non-circular fibers exist. This paper presents a scattering model for elliptical fibers, which predicts that even small deviations from circularity produce important changes in the scattering distribution, and which disagrees with previous approximations for the effects of eccentricity. To confirm the models predictions, new scattering measurements of fibers from a wide range of hair types were made, using a new measurement device that provides a more complete and detailed picture of the light scattered by fibers than was previously possible. The measurements show features that conclusively match the models predictions, but they also contain a new forward-scattering E mode that is not predicted and has not been fully described before. The results of this paper indicate that an accurate and efficient method for computing scattering in elliptical cylinderssomething not provided in this paperis the correct model to use for realistic hair in the future, and that the new E mode should be included as well.

Virtual Rephotography: Novel View Prediction Error for 3D Reconstruction

The ultimate goal of many image-based modeling systems is to render photo-realistic novel views of a scene without visible artifacts. Existing evaluation metrics and benchmarks focus mainly on the geometric accuracy of the reconstructed model, which is, however, a poor predictor of visual accuracy. Furthermore, using only geometric accuracy by itself does not allow evaluating systems that either lack a geometric scene representation or utilize coarse proxy geometry. Examples include light field or image-based rendering systems. We propose a unified evaluation approach based on novel view prediction error that is able to analyze the visual quality of any method that can render novel views from input images. One of the key advantages of this approach is that it does not require ground truth geometry. This dramatically simplifies the creation of test datasets and benchmarks. It also allows us to evaluate the quality of an unknown scene during the acquisition and reconstruction process, which is useful for acquisition planning. We evaluate our approach on a range of methods including standard geometry-plus-texture pipelines as well as image-based rendering techniques, compare it to existing geometry-based benchmarks, and demonstrate its utility for a range of use cases.

Antialiasing Complex Global Illumination Effects in Path-space

We present the first method to efficiently and accurately predict antialiasing footprints to pre-filter color-, normal-, and displacement-mapped appearance in the context of multi-bounce global illumination. We derive Fourier spectra for radiance and importance functions that allow us to compute spatial-angular filtering footprints at path vertices, for both uni- and bi-directional path construction. We then use these footprints to antialias reflectance modulated by high-resolution color, normal, and displacement maps encountered along a path. In doing so, we also unify the traditional path-space formulation of light-transport with our frequency-space interpretation of global illumination pre-filtering. Our method is fully compatible with all existing single bounce pre-filtering appearance models, not restricted by path length, and easy to implement atop existing path-space renderers. We illustrate its effectiveness on several radiometrically complex scenarios where previous approaches either completely fail or require orders of magnitude more time to arrive at similarly high-quality results.

A Spatial Target Function for Metropolis Photon Tracing

The human visual system is sensitive to relative differences in luminance but light transport simulation algorithms based on Metropolis sampling often result in a highly non-uniform relative error distribution over the rendered image. We present a new target function for Metropolis photon tracing that ensures good stratification of photons leading to pixel estimates with equalized relative error. We develop a hierarchical scheme for progressive construction of the target function from paths sampled during rendering. As opposed to previous work that only defined a target function in the image plane, ours can be associated with spatial regions. This allows us to take advantage of illumination coherence to receive robust estimates of the target function while adapting to geometry discontinuities. To sample from this target function, we design a new replica exchange Metropolis scheme. We apply our algorithm in progressive photon mapping and show that it often outperforms alternative approaches in terms of image quality by a large margin.

Inverse Procedural Weather Design for 3D Urban Models

We present the first realistic, physically-based, real-time weather design tool for use in urban procedural modeling. We merge designing of a 3D urban model with a controlled long-lasting spatiotemporal simulation of weather. Using the same-scale version of Navier-Stokes equations used in state-of-the-art weather models, we present a novel simplified urban weather model for interactive graphics. Control of physically-based weather phenomena is accomplished via an inverse modeling methodology. In our results, we present several scenarios of forward design, inverse design with high-level and detailed-level weather control and optimization, as well as comparisons of our method against well-known weather simulation results and systems.

Decomposing Images into Layers via RGB-space Geometry

In digital image editing software, layers organize images. However, layers are not explicitly represented in the final image, and may never have existed for a physical painting or a photograph. We propose a technique to decompose an image into layers. In our decomposition, each layer represents a single-color coat of paint applied with varying opacity. Our decomposition is based on the image's RGB-space geometry. In RGB-space, the linear nature of the standard Porter-Duff "over" pixel compositing operation implies a geometric structure. The vertices of the convex hull of image pixels in RGB-space correspond to a palette of paint colors. These colors may be hidden and inaccessible to algorithms based on clustering visible colors. For our layer decomposition, users choose the palette size (degree of simplification to perform on the convex hull), as well as a layer order for the paint colors (vertices). We then solve a constrained optimization problem to find translucent, spatially coherent opacity for each layer, such that the composition of the layers reproduces the original image. We demonstrate the utility of the resulting decompositions for recoloring and object insertion. Our layers can be interpreted as generalized barycentric coordinates; we compare to these and other recoloring approaches.

PERFORM: Perceptual Approach for Adding OCEAN Personality to Human Motion using Laban Movement Analysis

A major goal of research on virtual humans is the animation of expressive characters that display distinct psychological attributes. Body motion is an effective way of portraying different personalities and differentiating characters. The purpose and contribution of this study is to describe a formal, broadly applicable, procedural, and empirically grounded association between personality and body motion and apply this association to modify a given virtual human body animation that can be represented by these formal concepts. Because the body movement of virtual characters may involve different choices of parameter sets depending on the context, situation or application, formulating a link from personality to body motion requires an intermediate step to assist generalization. For this intermediate step, we refer to Laban Movement Analysis, which is a movement analysis technique for systematically describing and evaluating human motion. We have developed an expressive human motion generation system with the help of movement experts and conducted a user study to explore how the psychologically validated OCEAN personality factors were perceived in motions with various Laban parameters. We have then applied our findings to procedurally animate expressive characters with personality, and validated the generalizability of our approach across different models and animations via another perception study.

Constructing Intrinsic Delaunay Triangulations from the Dual of Geodesic Voronoi Diagrams

Intrinsic Delaunay triangulation (IDT) is a fundamental data structure in computational geometry and computer graphics. However, except for some theoretical results, such as existence and uniqueness, little progress has been made towards computing IDT on simplicial surfaces. To date the only way for constructing IDTs is the edge-flipping algorithm, which iteratively flips the non-Delaunay edge to be locally Delaunay. Although the algorithm is conceptually simple and guarantees to stop in finite steps, it has no known time complexity. Moreover, the edge-flipping algorithm may produce non-regular triangulations, which contain self-loops and/or faces with only two edges. In this paper, we propose a new method for constructing IDT on manifold triangle meshes. Based on the duality of geodesic Voronoi diagrams, our method can guarantee the resultant IDTs are regular. Our method has a theoretical worst-case time complexity O(n^2\log n) for a mesh with n vertices. We observe that most real-world models are far from their Delaunay triangulations, thus, the edge-flipping algorithm takes many iterations to fix the non-Delaunay edges. In contrast, our method is non-iterative and insensitive to the number of non-Delaunay edges. Empirically, it runs in linear time O(n) on real-world models.

Momentum-mapped Inverted Pendulum Models for Controlling Dynamic Human Motions

Designing a unified framework for simulating multiple human behaviors has proven very difficult. In this paper, we present an approach for control system design that can generate animations that closely resemble a diverse set of captured reference motions: walking, running, and gymnastic behaviors such as a roundoff, Popa jump, hand walking, and backflip. We achieve this generalization by using a balancing strategy that relies on a new model, which we call the momentum-mapped inverted pendulum model (MMIPM). We analyze the reference motion in a pre-processing step to extract the important properties of balance for that particular motion. That information is contained in the sequence of footsteps/handholds and in the basic balance control of an inverted pendulum controller. At run-time, the controller plans a desired motion frame by frame based on the current estimate of the pendulum state and a predicted pendulum trajectory. By tracking this time-varying trajectory, our controller creates a character that dynamically balances, changes speed, makes turns, jumps and performs a backflip. The initial controller can be optimized to further improve the motion quality. We demonstrate the power of this approach by generating controllers that produce natural looking animations and are robust to changes in the environment, user commands and external disturbances.

Functional Characterization of Intrinsic and Extrinsic Geometry

We propose a novel way to capture and characterize distortion between pairs of shapes by extending the recently proposed framework of shape differences built on functional maps. We modify the original definition of shape differences slightly and prove that, after this change, the discrete metric is fully encoded in two shape difference operators and can be recovered by solving two linear systems of equations. Then, we introduce an extension of the shape difference operators using offset surfaces to capture extrinsic or embedding-dependent distortion, complementing the purely intrinsic nature of the original shape differences. Finally, we demonstrate that a set of four operators is complete, capturing intrinsic and extrinsic structure and fully encoding a shape up to rigid motion in both discrete and continuous settings. We highlight the usefulness of our constructions by showing the complementary nature of our extrinsic shape differences in capturing distortion ignored by previous approaches. We additionally provide examples where we recover local shape structure from the shape difference operators, suggesting shape editing and analysis tools based on manipulating shape differences.

Retrieval on Parametric Shape Collections

While collections of parametric shapes are growing in size and use, little progress has been made on the fundamental problem of shape-based matching and retrieval for parametric shapes in a collection. The search space for such collections is both discrete (number of shapes) and continuous (parameters values). In this work we propose representing this space using descriptors which have shown to be effective for single shape retrieval. While single shapes can be represented as points in a descriptor space, parametric shapes are mapped into larger continuous regions. For smooth descriptors, we can assume that these regions are bounded low dimensional manifolds where the dimensionality is given by the number of shape parameters. We propose representing these manifolds with a set of primitives, namely: points and bounded tangent spaces. Our algorithm describes how to define these primitives and how to use them to construct a manifold approximation that allows accurate and fast retrieval. We show how to compute decision variables with no need for empirical parameters adjustments and discuss theoretical guarantees on retrial accuracy. We validate our approach with experiments that use different types of descriptors on a collection of shapes from multiple categories.

Bibliometrics

Publication Years 1982-2016
Publication Count 2758
Citation Count 75937
Available for Download 2756
Downloads (6 weeks) 37185
Downloads (12 Months) 314931
Downloads (cumulative) 3169911
Average downloads per article 1150
Average citations per article 28
First Name Last Name Award
Aseem Agarwala ACM Doctoral Dissertation Award
Honorable Mention (2006) ACM Doctoral Dissertation Award
Honorable Mention (2006)
Benjamin B Bederson ACM Distinguished Member (2011)
Kellogg S. Booth ACM Distinguished Member (2009)
ACM Senior Member (2009)
Ronan Boulic ACM Senior Member (2006)
Achi Brandt SIAM/ACM Prize in Computational Science and Engineering (2005)
Frederick Brooks ACM-IEEE CS Eckert-Mauchly Award (2004)
ACM A. M. Turing Award (1999)
ACM AAAI Allen Newell Award (1994)
ACM Distinguished Service Award (1987)
Bing-Yu Chen ACM Senior Member (2011)
Seth Cooper ACM Doctoral Dissertation Award (2011)
Thomas DeFanti Outstanding Contribution to ACM Award (1988)
James Demmel ACM Paris Kanellakis Theory and Practice Award (2014)
David S Ebert ACM Senior Member (2009)
Irfan A Essa ACM Senior Member (2008)
Robert M Geist ACM Distinguished Member (2009)
Leonidas J Guibas ACM AAAI Allen Newell Award (2007)
David Harel ACM Software System Award (2007)
ACM Karl V. Karlstrom Outstanding Educator Award (1992)
Aaron Hertzmann ACM Distinguished Member (2015)
Derek Hoiem ACM Doctoral Dissertation Award
Honorable Mention (2008) ACM Doctoral Dissertation Award
Honorable Mention (2008)
Hui Huang ACM Senior Member (2016)
Warren A. Hunt ACM Distinguished Member (2015)
ACM Senior Member (2013)
Victoria L Interrante ACM Senior Member (2007)
Takeo Kanade ACM AAAI Allen Newell Award (2010)
Dina Katabi ACM Grace Murray Hopper Award (2012)
ACM Doctoral Dissertation Award
Honorable Mention (2003)
Donald E Knuth ACM Software System Award (1986)
ACM A. M. Turing Award (1974)
ACM Grace Murray Hopper Award (1971)
Daphne Koller ACM Prize in Computing (2007)
James F. O'Brien ACM Distinguished Member (2009)
Ming Ouhyoung ACM Senior Member (2007)
Ravi Ramamoorthi ACM Distinguished Member (2015)
Sandy Ressler ACM Senior Member (2009)
Jarek Rossignac ACM Senior Member (2013)
Holly E Rushmeier ACM Distinguished Member (2006)
Hanan Samet ACM Paris Kanellakis Theory and Practice Award (2011)
Jeremy Sugerman ACM Software System Award (2009)
Subhash Suri ACM Distinguished Member (2007)
Ivan Sutherland ACM Software System Award (1993)
ACM A. M. Turing Award (1988)
Matthew A Turk ACM Senior Member (2007)
Jeffrey S Vetter ACM Distinguished Member (2012)
ACM Gordon Bell Prize
Performance (2010)
KangKang Yin ACM Senior Member (2016)
Eugene Zhang ACM Senior Member (2010)
Yuanyuan Zhou ACM Distinguished Member (2011)
Denis Zorin ACM Gordon Bell Prize
Performance (2010) ACM Gordon Bell Prize
Performance (2010)
Michael J Zyda ACM Senior Member (2009)
Andries van Dam ACM Karl V. Karlstrom Outstanding Educator Award (1993)

First Name Last Name Paper Counts
Daniel Cohen-Or 81
Frédo Durand 70
Baining Guo 61
Wojciech Matusik 61
Hans Seidel 58
Markus Gross 57
Kun Zhou 51
Ravi Ramamoorthi 43
Heungyeung Shum 43
Niloy Mitra 41
Ramesh Raskar 34
Szymon Rusinkiewicz 32
Jessica Hodgins 32
Kavita Bala 31
Pat Hanrahan 30
Hugues Hoppe 30
Eitan Grinspun 30
Doug James 30
Maneesh Agrawala 30
Fabio Pellacini 30
Peter Wonka 29
James O'Brien 29
Mathieu Desbrun 29
Alla Sheffer 28
Steve Marschner 28
Mark Pauly 27
Jovan Popovíc 27
Hujun Bao 26
Henrik Jensen 26
Zoran Popović 26
Leonidas Guibas 25
Takeo Igarashi 24
Dani Lischinski 24
Yizhou Yu 23
Yaron Lipman 23
Hanspeter Pfister 23
Shimin Hu 22
Bernd Bickel 22
Olga Sorkine-Hornung 22
Richard Szeliski 22
Greg Turk 21
Shree Nayar 21
Denis Zorin 21
Wolfgang Heidrich 21
Adam Finkelstein 21
Ronald Fedkiw 20
Michael Cohen 20
Tientsin Wong 20
Wojciech Jarosz 20
Helmut Pottmann 19
Thomas Funkhouser 19
Derek Nowrouzezahrai 19
Jason Lawrence 19
Oliver Deussen 19
Michiel Van De Panne 19
Peter Schröder 19
Vladlen Koltun 19
Liyi Wei 19
David Salesin 19
Jan Kautz 19
Raanan Fattal 18
Aseem Agarwala 18
Hao Zhang 18
George Drettakis 18
Wilmot Li 18
Dinesh Pai 17
Pieter Peers 17
Robert Bridson 17
Matthias Zwicker 17
Chris Wojtan 17
Paul Debevec 17
Stelian Coros 16
Jaakko Lehtinen 16
Julie Dorsey 16
C Liu 16
Olga Sorkine 16
William Freeman 16
Bernhard Thomaszewski 16
Christian Theobalt 16
Daniele Panozzo 15
Dinesh Manocha 15
Sylvain Lefebvre 15
Karol Myszkowski 15
Jinxiang Chai 15
John Snyder 15
Eli Shechtman 15
Johannes Kopf 15
Ariel Shamir 15
Xin Tong 14
Leif Kobbelt 14
Brian Curless 14
Andrei Sharf 14
Diego Gutierrez 14
Tomas Akenine-Möller 14
Marc Alexa 14
Stephen Lin 14
Bruce Walter 14
Tobias Ritschel 14
Sylvain Paris 14
Adrien Bousseau 14
Tao Ju 13
Ilya Baran 13
Diego Nehab 13
Weiwei Xu 13
Jernej Barbič 13
Marie Cani 13
Tony DeRose 13
Marc Levoy 13
Chikeung Tang 13
Carol O'Sullivan 13
Alec Jacobson 13
Jian Sun 13
Matthias Nießner 13
Yang Liu 13
Shuang Zhao 13
Baoquan Chen 13
Donald Greenberg 13
Gordon Wetzstein 12
Aaron Hertzmann 12
Huamin Wang 12
Andrew Selle 12
Wenping Wang 12
Ming Lin 12
Hao Zhang 12
Connelly Barnes 12
Jin Huang 12
Bedřich Beneš 12
Holly Rushmeier 12
Tim Weyrich 12
Craig Gotsman 12
Ladislav Kavan 12
Bruno Lévy 12
Alexander Hornung 12
Changxi Zheng 12
Thabo Beeler 12
Jehee Lee 12
Piotr Didyk 12
Matthew Fisher 12
Dan Goldman 12
Ulrich Pinkall 11
Toshiya Hachisuka 11
Shinjiro Sueda 11
Yue Dong 11
Johannes Wallner 11
Etienne Vouga 11
Douglas Lanman 11
Robert Sumner 11
Hui Huang 11
Jonathan Ragan-Kelley 11
Kayvon Fatahalian 11
Adrien Treuille 11
Steven Seitz 10
Jiaya Jia 10
Rasmus Tamstorf 10
Kai Xu 10
Eftychios Sifakis 10
Felix Heide 10
Derek Bradley 10
Singbing Kang 10
Adam Bargteil 10
Marc Stamminger 10
Marco Tarini 10
Matthias Hullin 10
Tongyee Lee 10
John Hart 10
Yingqing Xu 10
Miguel Otaduy 10
Daniel Aliaga 10
Jiaping Wang 10
Timo Aila 10
Alexei Efros 10
Wenzel Jakob 10
Markus Gross 10
Pierre Alliez 10
Scott Schaefer 10
KangKang Yin 10
Yiying Tong 10
François Sillion 10
Elmar Eisemann 10
Ofir Weber 9
Shahram Izadi 9
Rahul Narain 9
Michael Kazhdan 9
Jue Wang 9
Fernando De Goes 9
Keenan Crane 9
Rafał Mantiuk 9
Phengann Heng 9
Minglun Gong 9
Joe Warren 9
Baoquan Chen 9
Christopher Batty 9
Kalyan Sunkavalli 9
Philip Dutré 9
Rui Wang 9
Jaroslav Křivánek 9
Nancy Pollard 9
Jarek Rossignac 9
Danny Kaufman 9
Nils Thuerey 9
Nathan Carr 9
Hendrik Lensch 9
Sylvain Paris 9
Michael Zollhöfer 8
Todd Zickler 8
Charles Loop 8
Chiwing Fu 8
Pradeep Sen 8
Michael Gleicher 8
Marcus Magnor 8
David Bommes 8
Edward Adelson 8
Michael Goesele 8
François Faure 8
Long Quan 8
Tomoyuki Nishita 8
Peter Belhumeur 8
Mark Horowitz 8
Nuttapong Chentanez 8
Matt Uyttendaele 8
Michael Wimmer 8
Michael Garland 8
James Tompkin 8
Craig Donner 8
Cyril Soler 8
Miloš Hašan 8
Vladimir Kim 8
Andrew Adams 8
Karan Singh 8
Qiming Hou 8
Hongbo Fu 8
Peter Sloan 8
Xin Tong 8
Carsten Dachsbacher 8
Oliver Van Kaick 8
Ping Tan 8
Nicolas Ray 7
Michael Rubinstein 7
Linjie Luo 7
Ravi Ramamoorthi 7
Theodore Kim 7
Ayellet Tal 7
Matthias Müller 7
Victor Ostromoukhov 7
Doug DeCarlo 7
Peter Shirley 7
Paolo Cignoni 7
John Hughes 7
Pitchaya Sitthi-amorn 7
John Owens 7
Floraine Berthouzoz 7
Ligang Liu 7
Nico Pietroni 7
Yoshinori Dobashi 7
Yotam Gingold 7
Demetri Terzopoulos 7
Maks Ovsjanikov 7
Brian Barsky 7
Joseph Teran 7
Marcel Campen 7
David Forsyth 7
Manuel Oliveira 7
Robert Sumner 7
Hao Li 7
Abhijeet Ghosh 7
Pascal Barla 7
Daniel Vlasic 7
Tamy Boubekeur 7
Jörg Peters 7
William Mark 7
Paul Beardsley 7
Noam Aigerman 6
Gilbert Bernstein 6
Emily Whiting 6
Okan Arikan 6
Erik Reinhard 6
Dongming Yan 6
Sungeui Yoon 6
Rachel McDonnell 6
Thorsten Thormählen 6
Max Wardetzky 6
Eugene Zhang 6
Leonard McMillan 6
Valerio Pascucci 6
Matthew Hirsch 6
Yongliang Yang 6
Evangelos Kalogerakis 6
Roger Hersch 6
David Levin 6
Ivo Ihrke 6
Chiewlan Tai 6
James Ferwerda 6
Kari Pulli 6
Hao Li 6
Yuting Ye 6
Ron Goldman 6
Thomas Funkhouser 6
Sofien Bouaziz 6
Yonghao Yue 6
Tianjia Shao 6
Chandrajit Bajaj 6
Elaine Cohen 6
John Lewis 6
Florence Bertails-Descoubes 6
Justin Solomon 6
Saikit Yeung 6
David Dobkin 6
Bart Adams 6
Noah Snavely 6
Solomon Boulos 6
Mirela Ben Chen 6
Junyong Noh 6
Enrico Puppo 6
Zeev Farbman 6
Mario Botsch 6
Andrew Nealen 6
Petrik Clarberg 6
Tim Hawkins 6
Yongjoon Lee 6
Eugene Fiume 6
Yaser Sheikh 6
Kurt Akeley 6
Ariel Shamir 6
Anat Levin 6
John Keyser 6
Frank Losasso 6
David Luebke 6
Pedro Sander 6
Boris Neubert 6
Xinguo Liu 6
Hyeongseok Ko 6
Brian Wyvill 6
Qixing Huang 6
Ramesh Raskar 5
Ares Lagae 5
Lvdi Wang 5
Charles Zitnick 5
Miika Aittala 5
Michael Black 5
Oliver Wang 5
Stephen DiVerdi 5
Martin Kilian 5
Xin Sun 5
Nils Thürey 5
Nicolas Bonneel 5
Philipp Slusallek 5
Yungyu Chuang 5
Gaël Guennebaud 5
Yanlin Weng 5
Michael Wand 5
Paul Kry 5
Aaron Hertzmann 5
Derek Bradley 5
Cem Yuksel 5
Carsten Rother 5
Samuli Laine 5
Johannes Schmid 5
Jonathan Shewchuk 5
James Hays 5
Yushuen Wang 5
Sebastian Martin 5
Christoph Von Tycowicz 5
Dinesh Manocha 5
Igor Mordatch 5
Jay Busch 5
Greg Ward 5
Nobuyuki Umetani 5
Makoto Okabe 5
Nicholas Vining 5
Moritz Bächer 5
John Anderson 5
David Harmon 5
Manolis Savva 5
Ren Ng 5
Jack Tumblin 5
Alec Rivers 5
Ravish Mehra 5
Klaus Hildebrandt 5
Aljoscha Smolic 5
Shimin Hu 5
Craig Schroeder 5
Chongyang Ma 5
Tunç Aydın 5
Joelle Thollot 5
Andreas Weber 5
Jonathantyler Moon 5
Jon Hasselgren, 5
Naga Govindaraju 5
Tobias Pfaff 5
Changxi Zheng 5
Ibraheem Alhashim 5
Daniel Sýkora 5
Stefan Jeschke 5
Graham Fyffe 5
Shai Avidan 5
David Levin 5
Thomas Sederberg 5
Yingge Qu 5
Reinhard Klein 5
Tim Foley 5
Lingqi Yan 5
Nicolas Holzschuch 5
Shiqing Xin 5
Patrick Mullen 5
Min Kim 5
Martin Bokeloh 5
Taku Komura 5
Olga Sorkine 5
Michael Nielsen 5
David Cohen-Steiner 5
Honghua Li 5
Ligang Liu 5
Amit Agrawal 5
Aleksey Golovinskiy 5
Zhong Ren 5
Ying He 5
Bingyu Chen 5
Roland Fleming 5
Seungyong Lee 5
Brian Guenter 5
Mingming Cheng 5
Christian Schulz 5
Ken Museth 5
Jianyuan Min 5
Amir Vaxman 5
Libin Liu 5
Gabriel Brostow 5
Sebastian Thrun 5
Christoph Bregler 5
Gabriel Taubin 5
Ulf Assarsson 5
James Gregson 5
Andrew Jones 5
Niloy Mitra 5
Min Tang 5
Ashish Myles 5
Jianmin Zheng 5
Jacob Munkberg 5
Ralph Martin 5
Christian Theobalt 5
Ruizhen Hu 5
Srinivasa Narasimhan 5
Yiying Tong 5
Xiaohan Shi 5
Arno Zinke 5
Menglei Chai 5
Xin Tong 5
Adam Arbree 5
Youngjun Kim 5
Carsten Dachsbacher 5
Siddhartha Chaudhuri 4
Martin Fuchs 4
Michael Burns 4
Philippe Beaudoin 4
Carsten Stoll 4
Gaurav Chaurasia 4
Cláudio Silva 4
Geoffrey Irving 4
Michael Brown 4
Dhruv Mahajan 4
Jack Wang 4
Damien Rohmer 4
Stéfanie Hahmann 4
Ari Rappoport 4
John Beatty 4
Leonid Sigal 4
Tiberiu Popa 4
Tamal Dey 4
Lin Lu 4
Neel Joshi 4
Lee Markosian 4
Karan Singh 4
Aditi Majumder 4
Frédo Durand 4
C Liu 4
Rasmus Barringer 4
Mira Dontcheva 4
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Yin Li 4
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Xi Wang 4
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Akash Garg 4
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Xin Tong 3
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Hongzhi Wu 3
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