1 code implementation • ECCV 2020 • Panos Achlioptas, Ahmed Abdelreheem, Fei Xia, Mohamed Elhoseiny, Leonidas Guibas
Due to the scarcity and unsuitability of existent 3D-oriented linguistic resources for this task, we first develop two large-scale and complementary visio-linguistic datasets: i) extbf{ extit{Sr3D}}, which contains 83. 5K template-based utterances leveraging extit{spatial relations} with other fine-grained object classes to localize a referred object in a given scene, and ii) extbf{ extit{Nr3D}} which contains 41. 5K extit{natural, free-form}, utterances collected by deploying a 2-player object reference game in 3D scenes.
1 code implementation • ECCV 2020 • Oshri Halimi, Ido Imanuel, Or Litany, Giovanni Trappolini, Emanuele Rodolà, Leonidas Guibas, Ron Kimmel
Here, we claim that observing part of an object which was previously acquired as a whole, one could deal with both partial matching and shape completion in a holistic manner.
no code implementations • 9 May 2022 • Abhijit Kundu, Kyle Genova, Xiaoqi Yin, Alireza Fathi, Caroline Pantofaru, Leonidas Guibas, Andrea Tagliasacchi, Frank Dellaert, Thomas Funkhouser
Our model builds a panoptic radiance field representation of any scene from just color images.
no code implementations • 28 Jan 2022 • Jiahan Li, Shitong Luo, Congyue Deng, Chaoran Cheng, Jiaqi Guan, Leonidas Guibas, Jian Peng, Jianzhu Ma
In this work, we propose the Directed Weight Neural Network for better capturing geometric relations among different amino acids.
no code implementations • 17 Dec 2021 • Mikaela Angelina Uy, Yen-Yu Chang, Minhyuk Sung, Purvi Goel, Joseph Lambourne, Tolga Birdal, Leonidas Guibas
We propose Point2Cyl, a supervised network transforming a raw 3D point cloud to a set of extrusion cylinders.
no code implementations • 17 Dec 2021 • Yuefan Shen, Yanchao Yang, Mi Yan, He Wang, Youyi Zheng, Leonidas Guibas
Here we propose a simple yet effective method for unsupervised domain adaptation on point clouds by employing a self-supervised task of learning geometry-aware implicits, which plays two critical roles in one shot.
no code implementations • ICLR 2022 • Chuanyu Pan, Yanchao Yang, Kaichun Mo, Yueqi Duan, Leonidas Guibas
We perform an extensive study of the key features of the proposed framework and analyze the characteristics of the learned representations.
no code implementations • 15 Dec 2021 • Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas Guibas, Jonathan Tremblay, Sameh Khamis, Tero Karras, Gordon Wetzstein
Unsupervised generation of high-quality multi-view-consistent images and 3D shapes using only collections of single-view 2D photographs has been a long-standing challenge.
1 code implementation • 13 Dec 2021 • Juil Koo, IAn Huang, Panos Achlioptas, Leonidas Guibas, Minhyuk Sung
We introduce PartGlot, a neural framework and associated architectures for learning semantic part segmentation of 3D shape geometry, based solely on part referential language.
no code implementations • ICLR 2022 • Qi Li, Kaichun Mo, Yanchao Yang, Hang Zhao, Leonidas Guibas
While most works focus on single-object or agent-object visual functionality and affordances, our work proposes to study a new kind of visual relationship that is also important to perceive and model -- inter-object functional relationships (e. g., a switch on the wall turns on or off the light, a remote control operates the TV).
no code implementations • 1 Dec 2021 • Yian Wang, Ruihai Wu, Kaichun Mo, Jiaqi Ke, Qingnan Fan, Leonidas Guibas, Hao Dong
Perceiving and interacting with 3D articulated objects, such as cabinets, doors, and faucets, pose particular challenges for future home-assistant robots performing daily tasks in human environments.
1 code implementation • NeurIPS 2021 • Tolga Birdal, Aaron Lou, Leonidas Guibas, Umut Şimşekli
Disobeying the classical wisdom of statistical learning theory, modern deep neural networks generalize well even though they typically contain millions of parameters.
no code implementations • NeurIPS 2021 • Xiaolong Li, Yijia Weng, Li Yi, Leonidas Guibas, A. Lynn Abbott, Shuran Song, He Wang
Category-level object pose estimation aims to find 6D object poses of previously unseen object instances from known categories without access to object CAD models.
1 code implementation • 22 Oct 2021 • Jiayi Chen, Yingda Yin, Tolga Birdal, Baoquan Chen, Leonidas Guibas, He Wang
Regressing rotations on SO(3) manifold using deep neural networks is an important yet unsolved problem.
no code implementations • 29 Jul 2021 • Yanchao Yang, Hanxiang Ren, He Wang, Bokui Shen, Qingnan Fan, Youyi Zheng, C. Karen Liu, Leonidas Guibas
Furthermore, to resolve ambiguities in converting the semantic images to semantic labels, we treat the view transformation network as a functional representation of an unknown mapping implied by the color images and propose functional label hallucination to generate pseudo-labels in the target domain.
1 code implementation • 27 Jul 2021 • Yuefan Shen, Yanchao Yang, Youyi Zheng, C. Karen Liu, Leonidas Guibas
We describe a method for unpaired realistic depth synthesis that learns diverse variations from the real-world depth scans and ensures geometric consistency between the synthetic and synthesized depth.
1 code implementation • 29 Jun 2021 • Kaichun Mo, Yuzhe Qin, Fanbo Xiang, Hao Su, Leonidas Guibas
Contrary to the vast literature in modeling, perceiving, and understanding agent-object (e. g., human-object, hand-object, robot-object) interaction in computer vision and robotics, very few past works have studied the task of object-object interaction, which also plays an important role in robotic manipulation and planning tasks.
no code implementations • ICLR 2022 • Ruihai Wu, Yan Zhao, Kaichun Mo, Zizheng Guo, Yian Wang, Tianhao Wu, Qingnan Fan, Xuelin Chen, Leonidas Guibas, Hao Dong
In this paper, we propose object-centric actionable visual priors as a novel perception-interaction handshaking point that the perception system outputs more actionable guidance than kinematic structure estimation, by predicting dense geometry-aware, interaction-aware, and task-aware visual action affordance and trajectory proposals.
no code implementations • NeurIPS 2021 • Wamiq Reyaz Para, Shariq Farooq Bhat, Paul Guerrero, Tom Kelly, Niloy Mitra, Leonidas Guibas, Peter Wonka
Sketches can be represented as graphs, with the primitives as nodes and the constraints as edges.
no code implementations • NeurIPS 2021 • Xiaolong Li, Yijia Weng, Li Yi, Leonidas Guibas, A. Lynn Abbott, Shuran Song, He Wang
To reduce the huge amount of pose annotations needed for category-level learning, we propose for the first time a self-supervised learning framework to estimate category-level 6D object pose from single 3D point clouds.
no code implementations • 17 May 2021 • Ge Zhang, Or Litany, Srinath Sridhar, Leonidas Guibas
We present StrobeNet, a method for category-level 3D reconstruction of articulating objects from one or more unposed RGB images.
3 code implementations • ICCV 2021 • Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, Leonidas Guibas
Invariance and equivariance to the rotation group have been widely discussed in the 3D deep learning community for pointclouds.
1 code implementation • 31 Mar 2021 • Luca Moschella, Simone Melzi, Luca Cosmo, Filippo Maggioli, Or Litany, Maks Ovsjanikov, Leonidas Guibas, Emanuele Rodolà
Spectral geometric methods have brought revolutionary changes to the field of geometry processing.
no code implementations • CVPR 2021 • Tolga Birdal, Vladislav Golyanik, Christian Theobalt, Leonidas Guibas
We present QuantumSync, the first quantum algorithm for solving a synchronization problem in the context of computer vision.
1 code implementation • CVPR 2021 • Mikaela Angelina Uy, Vladimir G. Kim, Minhyuk Sung, Noam Aigerman, Siddhartha Chaudhuri, Leonidas Guibas
In fact, we use the embedding space to guide the shape pairs used to train the deformation module, so that it invests its capacity in learning deformations between meaningful shape pairs.
2 code implementations • CVPR 2021 • Panos Achlioptas, Maks Ovsjanikov, Kilichbek Haydarov, Mohamed Elhoseiny, Leonidas Guibas
We present a novel large-scale dataset and accompanying machine learning models aimed at providing a detailed understanding of the interplay between visual content, its emotional effect, and explanations for the latter in language.
1 code implementation • CVPR 2021 • Jiahui Huang, He Wang, Tolga Birdal, Minhyuk Sung, Federica Arrigoni, Shi-Min Hu, Leonidas Guibas
We present MultiBodySync, a novel, end-to-end trainable multi-body motion segmentation and rigid registration framework for multiple input 3D point clouds.
1 code implementation • ICCV 2021 • Kaichun Mo, Leonidas Guibas, Mustafa Mukadam, Abhinav Gupta, Shubham Tulsiani
One of the fundamental goals of visual perception is to allow agents to meaningfully interact with their environment.
no code implementations • 20 Dec 2020 • Haowen Deng, Mai Bui, Nassir Navab, Leonidas Guibas, Slobodan Ilic, Tolga Birdal
For the former we contributed our own dataset composed of five indoor scenes where it is unavoidable to capture images corresponding to views that are hard to uniquely identify.
1 code implementation • CVPR 2021 • Siyan Dong, Qingnan Fan, He Wang, Ji Shi, Li Yi, Thomas Funkhouser, Baoquan Chen, Leonidas Guibas
Localizing the camera in a known indoor environment is a key building block for scene mapping, robot navigation, AR, etc.
no code implementations • 8 Dec 2020 • Qihang Fang, Yingda Yin, Qingnan Fan, Fei Xia, Siyan Dong, Sheng Wang, Jue Wang, Leonidas Guibas, Baoquan Chen
The past solutions are mostly based on Markov Localization, which reduces the position-wise camera uncertainty for localization.
no code implementations • ICCV 2021 • Wamiq Para, Paul Guerrero, Tom Kelly, Leonidas Guibas, Peter Wonka
We generate layouts in three steps.
2 code implementations • NeurIPS 2020 • Jialei Huang, Guanqi Zhan, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas Guibas, Hao Dong
Analogous to buying an IKEA furniture, given a set of 3D parts that can assemble a single shape, an intelligent agent needs to perceive the 3D part geometry, reason to propose pose estimations for the input parts, and finally call robotic planning and control routines for actuation.
1 code implementation • 14 Jun 2020 • Chiyu "Max" Jiang, Jingwei Huang, Andrea Tagliasacchi, Leonidas Guibas
We illustrate the effectiveness of this learned deformation space for various downstream applications, including shape generation via deformation, geometric style transfer, unsupervised learning of a consistent parameterization for entire classes of shapes, and shape interpolation.
1 code implementation • 7 Jun 2020 • Amir Zamir, Alexander Sax, Teresa Yeo, Oğuzhan Kar, Nikhil Cheerla, Rohan Suri, Zhangjie Cao, Jitendra Malik, Leonidas Guibas
Visual perception entails solving a wide set of tasks, e. g., object detection, depth estimation, etc.
1 code implementation • 23 May 2020 • Jingwei Huang, Chiyu Max Jiang, Baiqiang Leng, Bin Wang, Leonidas Guibas
Given a pair of shapes, our framework provides a novel shape feature-preserving mapping function that continuously deforms one model to the other by minimizing fitting and rigidity losses based on the non-rigid iterative-closest-point (ICP) algorithm.
Graphics Computational Geometry
1 code implementation • ECCV 2020 • Mai Bui, Tolga Birdal, Haowen Deng, Shadi Albarqouni, Leonidas Guibas, Slobodan Ilic, Nassir Navab
We present a multimodal camera relocalization framework that captures ambiguities and uncertainties with continuous mixture models defined on the manifold of camera poses.
1 code implementation • ECCV 2020 • Mikaela Angelina Uy, Jingwei Huang, Minhyuk Sung, Tolga Birdal, Leonidas Guibas
We introduce a new problem of retrieving 3D models that are deformable to a given query shape and present a novel deep deformation-aware embedding to solve this retrieval task.
no code implementations • CVPR 2020 • Tolga Birdal, Michael Arbel, Umut Şimşekli, Leonidas Guibas
We introduce a new paradigm, $\textit{measure synchronization}$, for synchronizing graphs with measure-valued edges.
no code implementations • ECCV 2020 • Yichen Li, Kaichun Mo, Lin Shao, Minhyuk Sung, Leonidas Guibas
Autonomous assembly is a crucial capability for robots in many applications.
1 code implementation • CVPR 2020 • Jingwei Huang, Justus Thies, Angela Dai, Abhijit Kundu, Chiyu Max Jiang, Leonidas Guibas, Matthias Nießner, Thomas Funkhouser
In this work, we present a novel approach for color texture generation using a conditional adversarial loss obtained from weakly-supervised views.
no code implementations • 26 Feb 2020 • Or Litany, Ari Morcos, Srinath Sridhar, Leonidas Guibas, Judy Hoffman
We seek to learn a representation on a large annotated data source that generalizes to a target domain using limited new supervision.
no code implementations • 6 Feb 2020 • Zhangsihao Yang, Or Litany, Tolga Birdal, Srinath Sridhar, Leonidas Guibas
In this work, we wish to challenge this practice and use a neural network to learn descriptors directly from the raw mesh.
1 code implementation • 27 Jan 2020 • Oshri Halimi, Ido Imanuel, Or Litany, Giovanni Trappolini, Emanuele Rodolà, Leonidas Guibas, Ron Kimmel
Here, we claim that observing part of an object which was previously acquired as a whole, one could deal with both partial matching and shape completion in a holistic manner.
2 code implementations • 21 Jan 2020 • Christiane Sommer, Yumin Sun, Leonidas Guibas, Daniel Cremers, Tolga Birdal
We propose a new method for segmentation-free joint estimation of orthogonal planes, their intersection lines, relationship graph and corners lying at the intersection of three orthogonal planes.
1 code implementation • ECCV 2020 • Jeffrey O. Zhang, Alexander Sax, Amir Zamir, Leonidas Guibas, Jitendra Malik
When training a neural network for a desired task, one may prefer to adapt a pre-trained network rather than starting from randomly initialized weights.
2 code implementations • ECCV 2020 • Yongheng Zhao, Tolga Birdal, Jan Eric Lenssen, Emanuele Menegatti, Leonidas Guibas, Federico Tombari
We present a 3D capsule module for processing point clouds that is equivariant to 3D rotations and translations, as well as invariant to permutations of the input points.
2 code implementations • CVPR 2020 • Xiaolong Li, He Wang, Li Yi, Leonidas Guibas, A. Lynn Abbott, Shuran Song
We develop a deep network based on PointNet++ that predicts ANCSH from a single depth point cloud, including part segmentation, normalized coordinates, and joint parameters in the canonical object space.
1 code implementation • 23 Dec 2019 • Alexander Sax, Jeffrey O. Zhang, Bradley Emi, Amir Zamir, Silvio Savarese, Leonidas Guibas, Jitendra Malik
How much does having visual priors about the world (e. g. the fact that the world is 3D) assist in learning to perform downstream motor tasks (e. g. navigating a complex environment)?
1 code implementation • ICCV 2019 • Manuel Dahnert, Angela Dai, Leonidas Guibas, Matthias Nießner
We propose a novel approach to learn a joint embedding space between scan and CAD geometry, where semantically similar objects from both domains lie close together.
1 code implementation • ICML 2020 • Trevor Standley, Amir R. Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, Silvio Savarese
Many computer vision applications require solving multiple tasks in real-time.
1 code implementation • ICCV 2019 • Ruqi Huang, Marie-Julie Rakotosaona, Panos Achlioptas, Leonidas Guibas, Maks Ovsjanikov
This paper proposes a learning-based framework for reconstructing 3D shapes from functional operators, compactly encoded as small-sized matrices.
1 code implementation • ICCV 2019 • Jingwei Huang, Yichao Zhou, Thomas Funkhouser, Leonidas Guibas
In this work, we introduce the novel problem of identifying dense canonical 3D coordinate frames from a single RGB image.
no code implementations • CVPR 2020 • Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri, Li Yi, Leonidas Guibas, Hao Zhang
While the part prior network can be trained with noisy and inconsistently segmented shapes, the final output of AdaCoSeg is a consistent part labeling for the input set, with each shape segmented into up to (a user-specified) K parts.
1 code implementation • CVPR 2019 • Xiangru Huang, Zhenxiao Liang, Xiaowei Zhou, Yao Xie, Leonidas Guibas, Qi-Xing Huang
Our approach alternates between transformation synchronization using weighted relative transformations and predicting new weights of the input relative transformations using a neural network.
no code implementations • ICCV 2019 • Anastasia Dubrovina, Fei Xia, Panos Achlioptas, Mira Shalah, Raphael Groscot, Leonidas Guibas
We present a novel neural network architecture, termed Decomposer-Composer, for semantic structure-aware 3D shape modeling.
1 code implementation • 31 Dec 2018 • Alexander Sax, Bradley Emi, Amir R. Zamir, Leonidas Guibas, Silvio Savarese, Jitendra Malik
This skill set (hereafter mid-level perception) provides the policy with a more processed state of the world compared to raw images.
1 code implementation • CVPR 2019 • Li Yi, Wang Zhao, He Wang, Minhyuk Sung, Leonidas Guibas
We introduce a novel 3D object proposal approach named Generative Shape Proposal Network (GSPN) for instance segmentation in point cloud data.
Ranked #16 on
3D Object Detection
on ScanNetV2
1 code implementation • CVPR 2019 • Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkhouser, Matthias Nießner, Leonidas Guibas
We introduce, TextureNet, a neural network architecture designed to extract features from high-resolution signals associated with 3D surface meshes (e. g., color texture maps).
Ranked #11 on
Semantic Segmentation
on ScanNet
2 code implementations • CVPR 2019 • Lingxiao Li, Minhyuk Sung, Anastasia Dubrovina, Li Yi, Leonidas Guibas
Fitting geometric primitives to 3D point cloud data bridges a gap between low-level digitized 3D data and high-level structural information on the underlying 3D shapes.
1 code implementation • 19 Sep 2018 • Li Yi, Haibin Huang, Difan Liu, Evangelos Kalogerakis, Hao Su, Leonidas Guibas
In this paper, we explore how the observation of different articulation states provides evidence for part structure and motion of 3D objects.
1 code implementation • 4 Jul 2018 • Minhyuk Sung, Anastasia Dubrovina, Vladimir G. Kim, Leonidas Guibas
Modeling relations between components of 3D objects is essential for many geometry editing tasks.
Graphics I.3.5
1 code implementation • ICLR 2019 • Jan Svoboda, Jonathan Masci, Federico Monti, Michael M. Bronstein, Leonidas Guibas
Deep learning systems have become ubiquitous in many aspects of our lives.
1 code implementation • NeurIPS 2018 • Minhyuk Sung, Hao Su, Ronald Yu, Leonidas Guibas
Even though our shapes have independent discretizations and no functional correspondences are provided, the network is able to generate latent bases, in a consistent order, that reflect the shared semantic structure among the shapes.
1 code implementation • CVPR 2018 • Amir Zamir, Alexander Sax, William Shen, Leonidas Guibas, Jitendra Malik, Silvio Savarese
The product is a computational taxonomic map for task transfer learning.
no code implementations • CVPR 2013 • Nan Hu, Raif M. Rustamov, Leonidas Guibas
In this paper, we consider the weighted graph matching problem with partially disclosed correspondences between a number of anchor nodes.
2 code implementations • 5 Feb 2018 • Jingwei Huang, Hao Su, Leonidas Guibas
In this paper, we describe a robust algorithm for 2-Manifold generation of various kinds of ShapeNet Models.
Computational Geometry
1 code implementation • 17 Oct 2017 • Li Yi, Lin Shao, Manolis Savva, Haibin Huang, Yang Zhou, Qirui Wang, Benjamin Graham, Martin Engelcke, Roman Klokov, Victor Lempitsky, Yuan Gan, Pengyu Wang, Kun Liu, Fenggen Yu, Panpan Shui, Bingyang Hu, Yan Zhang, Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Minki Jeong, Jaehoon Choi, Changick Kim, Angom Geetchandra, Narasimha Murthy, Bhargava Ramu, Bharadwaj Manda, M. Ramanathan, Gautam Kumar, P Preetham, Siddharth Srivastava, Swati Bhugra, Brejesh lall, Christian Haene, Shubham Tulsiani, Jitendra Malik, Jared Lafer, Ramsey Jones, Siyuan Li, Jie Lu, Shi Jin, Jingyi Yu, Qi-Xing Huang, Evangelos Kalogerakis, Silvio Savarese, Pat Hanrahan, Thomas Funkhouser, Hao Su, Leonidas Guibas
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
1 code implementation • 6 Aug 2017 • Minhyuk Sung, Hao Su, Vladimir G. Kim, Siddhartha Chaudhuri, Leonidas Guibas
The combinatorial nature of part arrangements poses another challenge, since the retrieval network is not a function: several complements can be appropriate for the same input.
Graphics I.3.5
3 code implementations • ICML 2018 • Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas Guibas
Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling.
no code implementations • 5 May 2017 • Jun Li, Kai Xu, Siddhartha Chaudhuri, Ersin Yumer, Hao Zhang, Leonidas Guibas
We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures.
no code implementations • CVPR 2018 • Cewu Lu, Hao Su, Yongyi Lu, Li Yi, Chi-Keung Tang, Leonidas Guibas
Important high-level vision tasks such as human-object interaction, image captioning and robotic manipulation require rich semantic descriptions of objects at part level.
no code implementations • CVPR 2017 • Li Yi, Hao Su, Xingwen Guo, Leonidas Guibas
To enable the prediction of vertex functions on them by convolutional neural networks, we resort to spectral CNN method that enables weight sharing by parameterizing kernels in the spectral domain spanned by graph laplacian eigenbases.
Ranked #24 on
3D Part Segmentation
on ShapeNet-Part
4 code implementations • CVPR 2017 • Haoqiang Fan, Hao Su, Leonidas Guibas
Our final solution is a conditional shape sampler, capable of predicting multiple plausible 3D point clouds from an input image.
Ranked #2 on
3D Reconstruction
on Data3D−R2N2
(using extra training data)
3D Object Reconstruction From A Single Image
3D Reconstruction
no code implementations • CVPR 2018 • Nan Hu, Qi-Xing Huang, Boris Thibert, Leonidas Guibas
In this paper we propose an optimization-based framework to multiple object matching.
13 code implementations • 9 Dec 2015 • Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qi-Xing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, Fisher Yu
We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects.
6 code implementations • NeurIPS 2015 • Chris Piech, Jonathan Spencer, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas Guibas, Jascha Sohl-Dickstein
Knowledge tracing---where a machine models the knowledge of a student as they interact with coursework---is a well established problem in computer supported education.
Ranked #1 on
Knowledge Tracing
on Assistments
no code implementations • 22 May 2015 • Chris Piech, Jonathan Huang, Andy Nguyen, Mike Phulsuksombati, Mehran Sahami, Leonidas Guibas
Providing feedback, both assessing final work and giving hints to stuck students, is difficult for open-ended assignments in massive online classes which can range from thousands to millions of students.
4 code implementations • ICCV 2015 • Hao Su, Charles R. Qi, Yangyan Li, Leonidas Guibas
Object viewpoint estimation from 2D images is an essential task in computer vision.
no code implementations • 26 Nov 2014 • Hao Su, Fan Wang, Li Yi, Leonidas Guibas
In this paper, given a single input image of an object, we synthesize new features for other views of the same object.
no code implementations • 19 May 2014 • Qixing Huang, Yuxin Chen, Leonidas Guibas
Maximum a posteriori (MAP) inference over discrete Markov random fields is a fundamental task spanning a wide spectrum of real-world applications, which is known to be NP-hard for general graphs.
no code implementations • CVPR 2014 • Nan Hu, Raif M. Rustamov, Leonidas Guibas
We also introduce the pairwise heat kernel distance as a stable second order compatibility term; we justify its plausibility by showing that in a certain limiting case it converges to the classical adjacency matrix-based second order compatibility function.