mtybilly/Instruct2DS huggingface dataset:
FAUST: Dataset and Evaluation for 3D Mesh Registration
Authors: Federica Bogo, Javier Romero, Matthew Loper, Michael J. Black
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: New scanning technologies are increasing the importance of 3D mesh data and the need for algorithms that can reliably align it. Surface registration is important for building full 3D models from partial scans, creating statistical shape models, shape retrieval, and tracking. The problem is particularly challenging for non-rigid and articulated objects like human bodies. While the challenges of real-world data registration are not present in existing synthetic datasets, establishing ground-truth correspondences for real 3D scans is difficult. We address this with a novel mesh registration technique that combines 3D shape and appearance information to produce high-quality alignments. We define a new dataset called FAUST that contains 300 scans of 10 people in a wide range of poses together with an evaluation methodology. To achieve accurate registration, we paint the subjects with high-frequency textures and use an extensive validation process to ensure accurate ground truth. We find that current shape registration methods have trouble with this real-world data. The dataset and evaluation website are available for research purposes at http://faust.is.tue.mpg.de.
Optimizing Over Radial Kernels on Compact Manifolds
Authors: Sadeep Jayasumana, Richard Hartley, Mathieu Salzmann, Hongdong Li, Mehrtash Harandi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We tackle the problem of optimizing over all possible positive definite radial kernels on Riemannian manifolds for classification. Kernel methods on Riemannian manifolds have recently become increasingly popular in computer vision. However, the number of known positive definite kernels on manifolds remain very limited. Furthermore, most kernels typically depend on at least one parameter that needs to be tuned for the problem at hand. A poor choice of kernel, or of parameter value, may yield significant performance drop-off. Here, we show that positive definite radial kernels on the unit $n$-sphere, the Grassmann manifold and Kendall's shape manifold can be expressed in a simple form whose parameters can be automatically optimized within a support vector machine framework. We demonstrate the benefits of our kernel learning algorithm on object, face, action and shape recognition.
Grassmann Averages for Scalable Robust PCA
Authors: Soren Hauberg, Aasa Feragen, Michael J. Black
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: As the collection of large datasets becomes increasingly automated, the occurrence of outliers will increase -- "big data" implies "big outliers''. While principal component analysis (PCA) is often used to reduce the size of data, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA do not scale beyond small-to-medium sized datasets. To address this, we introduce the Grassmann Average (GA), which expresses dimensionality reduction as an average of the subspaces spanned by the data. Because averages can be efficiently computed, we immediately gain scalability. GA is inherently more robust than PCA, but we show that they coincide for Gaussian data. We exploit that averages can be made robust to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. Robustness can be with respect to vectors (subspaces) or elements of vectors; we focus on the latter and use a trimmed average. The resulting Trimmed Grassmann Average (TGA) is particularly appropriate for computer vision because it is robust to pixel outliers. The algorithm has low computational complexity and minimal memory requirements, making it scalable to "big noisy data." We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie.
Robust Subspace Segmentation with Block-diagonal Prior
Authors: Jiashi Feng, Zhouchen Lin, Huan Xu, Shuicheng Yan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: The subspace segmentation problem is addressed in this paper by effectively constructing an exactly block-diagonal sample affinity matrix. The block-diagonal structure is heavily desired for accurate sample clustering but is rather difficult to obtain. Most current state-of-the-art subspace segmentation methods (such as SSC and LRR) resort to alternative structural priors (such as sparseness and low-rankness) to construct the affinity matrix. In this work, we  directly pursue the block-diagonal structure by proposing a graph Laplacian constraint based formulation, and then develop an efficient stochastic subgradient  algorithm for optimization. Moreover, two new subspace segmentation methods, the block-diagonal SSC and LRR, are devised in this work. To the best of our knowledge, this is the first research attempt to explicitly pursue such a block-diagonal structure. Extensive experiments on face clustering, motion segmentation and graph construction for semi-supervised learning clearly demonstrate the superiority of our novelly proposed subspace segmentation methods.
Unsupervised One-Class Learning for Automatic Outlier Removal
Authors: Wei Liu, Gang Hua, John R. Smith
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Outliers are pervasive in many computer vision and pattern recognition problems. Automatically eliminating outliers scattering among practical data collections becomes increasingly important, especially for Internet inspired vision applications. In this paper, we propose a novel one-class learning approach which is robust to contamination of input training data and able to discover the outliers that corrupt one class of data source. Our approach works under a fully unsupervised manner, differing from traditional one-class learning supervised by known positive labels. By design, our approach optimizes a kernel-based max-margin objective which jointly learns a large margin one-class classifier and a soft label assignment for inliers and outliers. An alternating optimization algorithm is then designed to iteratively refine the classifier and the labeling, achieving a provably convergent solution in only a few iterations. Extensive experiments conducted on four image datasets in the presence of artificial and real-world outliers demonstrate that the proposed approach is considerably superior to the state-of-the-arts in obliterating outliers from contaminated one class of images, exhibiting strong robustness at a high outlier proportion up to 60%.
Smooth Representation Clustering
Authors: Han Hu, Zhouchen Lin, Jianjiang Feng, Jie Zhou
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Subspace clustering is a powerful technology for clustering data according to the underlying subspaces. Representation based methods are the most popular subspace clustering approach in recent years. In this paper, we analyze the grouping effect of representation based methods in depth. In particular, we introduce the enforced grouping effect conditions, which greatly facilitate the analysis of grouping effect. We further find that grouping effect is important for subspace clustering, which should be explicitly enforced in the data self-representation model, rather than implicitly implied by the model as in some prior work. Based on our analysis, we propose the SMooth Representation (SMR) model. We also propose a new affinity measure based on the grouping effect, which proves to be much more effective than the commonly used one. As a result, our SMR significantly outperforms the state-of-the-art ones on benchmark datasets.
Novel Methods for Multilinear Data Completion and De-noising Based on Tensor-SVD
Authors: Zemin Zhang, Gregory Ely, Shuchin Aeron, Ning Hao, Misha Kilmer
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: In this paper we propose novel methods for completion (from limited samples) and de-noising of multilinear (tensor) data and as an application consider 3-D and 4- D (color) video data completion and de-noising. We exploit the recently proposed tensor-Singular Value Decomposition (t-SVD)[11]. Based on t-SVD, the notion of multilinear rank and a related tensor nuclear norm was proposed in [11] to characterize informational and structural complexity of multilinear data. We first show that videos with linear camera motion can be represented more efficiently using t-SVD compared to the approaches based on vectorizing or flattening of the tensors. Since efficiency in representation implies efficiency in recovery, we outline a tensor nuclear norm penalized algorithm for video completion from missing entries. Application of the proposed algorithm for video recovery from missing entries is shown to yield a superior performance over existing methods. We also consider the problem of tensor robust Principal Component Analysis (PCA) for de-noising 3-D video data from sparse random corruptions. We show superior performance of our method compared to the matrix robust PCA adapted to this setting as proposed in [4].
Second-Order Shape Optimization for Geometric Inverse Problems in Vision
Authors: Jonathan Balzer, Stefano Soatto
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We develop a method for optimization in shape spaces, i.e., sets of surfaces modulo re-parametrization. Unlike previously proposed gradient flows, we achieve superlinear convergence rates through an approximation of the shape Hessian, which is generally hard to compute and suffers from a series of degeneracies. Our analysis highlights the role of mean curvature motion in comparison with first-order schemes: instead of surface area, our approach penalizes deformation, either by its Dirichlet energy or total variation, and hence does not suffer from shrinkage. The latter regularizer sparks the development of an alternating direction method of multipliers on triangular meshes. Therein, a conjugate-gradient solver enables us to bypass formation of the Gaussian normal equations appearing in the course of the overall optimization. We combine all of these ideas in a versatile geometric variation-regularized Levenberg-Marquardt-type method applicable to a variety of shape functionals, depending on intrinsic properties of the surface such as normal field and curvature as well as its embedding into space. Promising experimental results are reported.
l0 Norm Based Dictionary Learning by Proximal Methods with Global Convergence
Authors: Chenglong Bao, Hui Ji, Yuhui Quan, Zuowei Shen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Sparse coding and dictionary learning have seen their applications in many vision tasks, which usually is formulated as a non-convex optimization problem. Many iterative methods have been proposed to tackle such an optimization problem. However, it remains an open problem to have a method that is not only practically fast but also is globally convergent. In this paper, we proposed a fast proximal method for solving l0 norm based dictionary learning problems, and we proved that the whole sequence generated by the proposed method converges to a stationary point with sub-linear convergence rate. The benefit of having a fast and convergent dictionary learning method is demonstrated in the applications of image recovery and face recognition.
Adaptive Partial Differential Equation Learning for Visual Saliency Detection
Authors: Risheng Liu, Junjie Cao, Zhouchen Lin, Shiguang Shan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Partial Differential Equations (PDEs) have been successful in solving many low-level vision tasks. However, it is a challenging task to directly utilize PDEs for visual saliency detection due to the difficulty in incorporating human perception and high-level priors to a PDE system. Instead of designing PDEs with fixed formulation and boundary condition, this paper proposes a novel framework for adaptively learning a PDE system from an image for visual saliency detection. We assume that the saliency of image elements can be carried out from the relevances to the saliency seeds (i.e., the most representative salient elements). In this view, a general Linear Elliptic System with Dirichlet boundary (LESD) is introduced to model the diffusion from seeds to other relevant points. For a given image, we first learn a guidance map to fuse human prior knowledge to the diffusion system. Then by optimizing a discrete submodular function constrained with this LESD and a uniform matroid, the saliency seeds (i.e., boundary conditions) can be learnt for this image, thus achieving an optimal PDE system to model the evolution of visual saliency. Experimental results on various challenging image sets show the superiority of our proposed learning-based PDEs for visual saliency detection.
Robust Orthonormal Subspace Learning: Efficient Recovery of Corrupted Low-rank Matrices
Authors: Xianbiao Shu, Fatih Porikli, Narendra Ahuja
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Low-rank matrix recovery from a corrupted observation has many applications in computer vision. Conventional methods address this problem by iterating between nuclear norm minimization and sparsity minimization. However, iterative nuclear norm minimization is computationally prohibitive for large-scale data (e.g., video) analysis. In this paper, we propose a Robust Orthogonal Subspace Learning (ROSL) method to achieve efficient low-rank recovery. Our intuition is a novel rank measure on the low-rank matrix that imposes the group sparsity of its coefficients under orthonormal subspace. We present an efficient sparse coding algorithm to minimize this rank measure and recover the low-rank matrix at quadratic complexity of the matrix size. We give theoretical proof to validate that this rank measure is lower bounded by nuclear norm and it has the same global minimum as the latter. To further accelerate ROSL to linear complexity, we also describe a faster version (ROSL+) empowered by random sampling. Our extensive experiments demonstrate that both ROSL and ROSL+ provide superior efficiency against the state-of-the-art methods at the same level of recovery accuracy.
Reconstructing Storyline Graphs for Image Recommendation from Web Community Photos
Authors: Gunhee Kim, Eric P. Xing
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: In this paper, we investigate an approach for reconstructing storyline graphs from large-scale collections of Internet images, and optionally other side information such as friendship graphs. The storyline graphs can be an effective summary that visualizes various branching narrative structure of events or activities recurring across the input photo sets of a topic class. In order to explore further the usefulness of the storyline graphs, we leverage them to perform the image sequential prediction tasks, from which photo recommendation applications can benefit. We formulate the storyline reconstruction problem as an inference of sparse time-varying directed graphs, and develop an optimization algorithm that successfully addresses a number of key challenges of Web-scale problems, including global optimality, linear complexity, and easy parallelization. With experiments on more than 3.3 millions of images of 24 classes and user studies via Amazon Mechanical Turk, we show that the proposed algorithm improves other candidate methods for both storyline reconstruction and image prediction tasks.
Active Flattening of Curved Document Images via Two Structured Beams
Authors: Gaofeng Meng, Ying Wang, Shenquan Qu, Shiming Xiang, Chunhong Pan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Document images captured by a digital camera often suffer from serious geometric distortions. In this paper,we propose an active method to correct geometric distortions in a camera-captured document image. Unlike many passive  rectification methods that rely on text-lines or features extracted from images, our method uses two structured beams  illuminating upon the document page to recover two spatial curves. A developable surface is then interpolated to the curves by finding the correspondence between them. The developable surface is finally flattened onto a plane by solving a system of ordinary differential equations. Our method is a content independent approach and can restore a corrected document image of high accuracy with undistorted contents. Experimental results on a variety of real-captured document images demonstrate the effectiveness and efficiency of the proposed method.
Image-based Synthesis and Re-Synthesis of Viewpoints Guided by 3D Models
Authors: Konstantinos Rematas, Tobias Ritschel, Mario Fritz, Tinne Tuytelaars
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We propose a technique to use the structural information extracted from a set of 3D models of an object class to improve novel-view synthesis for images showing unknown instances of this class. These novel views can be used to "amplify" training image collections that typically contain only a low number of views or lack certain classes of views entirely (e.g. top views). We extract the correlation of position, normal, reflectance and appearance from computer-generated images of a few exemplars and use this information to infer new appearance for new instances. We show that our approach can improve performance of state-of-the-art detectors using real-world training data. Additional applications include guided versions of inpainting, 2D-to-3D conversion, super-resolution and non-local smoothing.
Bayesian View Synthesis and Image-Based Rendering Principles
Authors: Sergi Pujades, Frederic Devernay, Bastian Goldluecke
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: In this paper, we address the problem of synthesizing novel views from a set of input images. State of the art methods, such as the Unstructured Lumigraph, have been using heuristics to combine information from the original views, often using an explicit or implicit approximation of the scene geometry. While the proposed heuristics have been largely explored and proven to work effectively, a Bayesian formulation was recently introduced, formalizing some of the previously proposed heuristics, pointing out which physical phenomena could lie behind each. However, some important heuristics were still not taken into account and lack proper formalization.  We contribute a new physics-based generative model and the corresponding Maximum a Posteriori estimate, providing the desired unification between heuristics-based methods and a Bayesian formulation. The key point is to systematically consider the error induced by the uncertainty in the geometric proxy. We provide an extensive discussion, analyzing how the obtained equations explain the heuristics developed in previous methods. Furthermore, we show that our novel Bayesian model significantly improves the quality of novel views, in particular if the scene geometry estimate is inaccurate.
Fast MRF Optimization with Application to Depth Reconstruction
Authors: Qifeng Chen, Vladlen Koltun
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We describe a simple and fast algorithm for optimizing Markov random fields over images. The algorithm performs block coordinate descent by optimally updating a horizontal or vertical line in each step. While the algorithm is not as accurate as state-of-the-art MRF solvers on traditional benchmark problems, it is trivially parallelizable and produces competitive results in a fraction of a second. As an application, we develop an approach to increasing the accuracy of consumer depth cameras. The presented algorithm enables high-resolution MRF optimization at multiple frames per second and substantially increases the accuracy of the produced range images.
Exploiting Shading Cues in Kinect IR Images for Geometry Refinement
Authors: Gyeongmin Choe, Jaesik Park, Yu-Wing Tai, In So Kweon
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: In this paper, we propose a method to refine geometry of 3D meshes from the Kinect fusion by exploiting shading cues captured from the infrared (IR) camera of Kinect. A major benefit of using the Kinect IR camera instead of a RGB camera is that the IR images captured by Kinect are narrow band images which filtered out most undesired ambient light that makes our system robust to natural indoor illumination. We define a near light IR shading model which describes the captured intensity as a function of surface normals, albedo, lighting direction, and distance between a light source and surface points. To resolve ambiguity in our model between normals and distance, we utilize an initial 3D mesh from the Kinect fusion and multi-view information to reliably estimate surface details that were not reconstructed by the Kinect fusion. Our approach directly operates on a 3D mesh model for geometry refinement. The effectiveness of our approach is demonstrated through several challenging real-world examples.
Fast Rotation Search with Stereographic Projections for 3D Registration
Authors: Alvaro Parra Bustos, Tat-Jun Chin, David Suter
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Recently there has been a surge of interest to use branch-and-bound (bnb) optimisation for 3D point cloud registration. While bnb guarantees globally optimal solutions, it is usually too slow to be practical. A fundamental source of difficulty is the search for the rotation parameters in the 3D rigid transform. In this work, assuming that the translation parameters are known, we focus on constructing a fast rotation search algorithm. With respect to an inherently robust geometric matching criterion, we propose a novel bounding function for bnb that allows rapid evaluation. Underpinning our bounding function is the usage of stereographic projections to precompute and spatially index all possible point matches. This yields a robust and global algorithm that is significantly faster than previous methods.  To conduct full 3D registration, the translation can be supplied by 3D feature matching, or by another optimisation framework that provides the translation. On various challenging point clouds, including those taken out of lab settings, our approach demonstrates superior efficiency.
Local Readjustment for High-Resolution 3D Reconstruction
Authors: Siyu Zhu, Tian Fang, Jianxiong Xiao, Long Quan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Global bundle adjustment usually converges to a non-zero residual and produces sub-optimal camera poses for local areas, which leads to loss of details for high- resolution reconstruction. Instead of trying harder to optimize everything globally, we argue that we should live with the non-zero residual and adapt the camera poses to local areas. To this end, we propose a segment-based approach to readjust the camera poses locally and improve the reconstruction for fine geometry details. The key idea is to partition the globally optimized structure from motion points into well-conditioned segments for re-optimization, reconstruct their geometry individually, and fuse everything back into a consistent global model. This significantly reduces severe propagated errors and estimation biases caused by the initial global adjustment. The results on several datasets demonstrate that this approach can significantly improve the reconstruction accuracy, while maintaining the consistency of the 3D structure between segments.
Turning Mobile Phones into 3D Scanners
Authors: Kalin Kolev, Petri Tanskanen, Pablo Speciale, Marc Pollefeys
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: In this paper, we propose an efficient and accurate scheme for the integration of multiple stereo-based depth measurements. For each provided depth map a confidence-based weight is assigned to each depth estimate by evaluating local geometry orientation, underlying camera setting and photometric evidence. Subsequently, all hypotheses are fused together into a compact and consistent 3D model. Thereby, visibility conflicts are identified and resolved, and fitting measurements are averaged with regard to their confidence scores. The individual stages of the proposed approach are validated by comparing it to two alternative techniques which rely on a conceptually different fusion scheme and a different confidence inference, respectively. Pursuing live 3D reconstruction on mobile devices as a primary goal, we demonstrate that the developed method can easily be integrated into a system for monocular interactive 3D modeling by substantially improving its accuracy while adding a negligible overhead to its performance and retaining its interactive potential.
T-Linkage: A Continuous Relaxation of J-Linkage for Multi-Model Fitting
Authors: Luca Magri, Andrea Fusiello
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: This paper presents an improvement of the J-linkage algorithm for fitting multiple instances of a model to noisy data corrupted by outliers. The binary preference analysis implemented by J-linkage is replaced by a continuous (soft, or fuzzy) generalization that proves to perform better than J-linkage on simulated data, and compares favorably with state of the art methods on public domain real datasets.
Motion-Depth: RGB-D Depth Map Enhancement with Motion and Depth in Complement
Authors: Tak-Wai Hui, King Ngi Ngan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Low-cost RGB-D imaging system such as Kinect is widely utilized for dense 3D reconstruction. However, RGB-D system generally suffers from two main problems. The spatial resolution of the depth image is low. The depth image often contains numerous holes where no depth measurements are available. This can be due to bad infra-red reflectance properties of some objects in the scene. Since the spatial resolution of the color image is generally higher than that of the depth image, this paper introduces a new method to enhance the depth images captured by a moving RGB-D system using the depth cues from the induced optical flow. We not only fill the holes in the raw depth images, but also recover fine details of the imaged scene. We address the problem of depth image enhancement by minimizing an energy functional. In order to reduce the computational complexity, we have treated the textured and homogeneous regions in the color images differently. Experimental results on several RGB-D sequences are provided to show the effectiveness of the proposed method.
Generalized Pupil-Centric Imaging and Analytical Calibration for a Non-frontal Camera
Authors: Avinash Kumar, Narendra Ahuja
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We consider the problem of calibrating a small field of view central perspective non-frontal camera whose lens and sensor planes may not be parallel to each other. This can be due to manufacturing defects or intentional tilting. Thus, as such all cameras can be modeled as being non-frontal with varying degrees. There are two approaches to model non- frontal cameras. The first one based on rotation parameterization of sensor non-frontalness/tilt increases the number of calibration parameters, thus requiring heuristics to initialize a few calibration parameters for the final non-linear optimization step. Additionally, for this parameterization, while it has been shown that pupil-centric imaging model leads to more accurate rotation estimates than a thin-lens imaging model, it has only been developed for a single axis lens-sensor tilt. But, in real cameras we can have arbitrary tilt. The second approach based on decentering distortion modeling is approximate as it can only handle small tilts and cannot explicitly estimate the sensor tilt. In this paper, we focus on rotation based non-frontal camera calibration and address the aforementioned problems of over-parameterization and inadequacy of existing pupil-centric imaging model. We first derive a generalized pupil-centric imaging model for arbitrary axis lens-sensor tilt. We then derive an analytical solution, in this setting, for a subset of calibration parameters including sensor rotation angles as a function of center of radial distortion (CoD). A radial alignment based constraint is then proposed to computationally estimate CoD leveraging on the proposed analytical solution. Our analytical technique also estimates pupil-centric parameters of entrance pupil location and optical focal length, which have typically been done optically. Given these analytical and computational calibration parameter estimates, we initialize the non-linear calibration optimization for a set of synthetic and real data captured from a non-frontal camera and show reduced pixel re-projection and undistortion errors compared to state of the art techniques in rotation and decentering based approaches to non-frontal camera calibration.
Geometric Urban Geo-Localization
Authors: Mayank Bansal, Kostas Daniilidis
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We propose a purely geometric correspondence-free approach to urban geo-localization using 3D point-ray features extracted from the Digital Elevation Map of an urban environment. We derive a novel formulation for estimating the camera pose locus using 3D-to-2D correspondence of a single point and a single direction alone. We show how this allows us to compute putative correspondences between building corners in the DEM and the query image by exhaustively combining pairs of point-ray features. Then, we employ the two-point method to estimate both the camera pose and compute correspondences between buildings in the DEM and the query image. Finally, we show that the computed camera poses can be efficiently ranked by a simple skyline projection step using building edges from the DEM. Our experimental evaluation illustrates the promise of a purely geometric approach to the urban geo-localization problem.
3D Reconstruction from Accidental Motion
Authors: Fisher Yu, David Gallup
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We have discovered that 3D reconstruction can be achieved from asingle still photographic capture due to accidental motions of thephotographer, even while attempting to hold the camera still.  Although these motions result in little baseline and therefore high depth uncertainty, in theory, we can combine many such measurements over the duration of the capture process (a few seconds) to achieve usable depth estimates.  Wepresent a novel 3D reconstruction system tailored for this problemthat produces depth maps from short video sequences from standard cameraswithout the need for multi-lens optics, active sensors, or intentionalmotions by the photographer.  This result leads to the possibilitythat depth maps of sufficient quality for RGB-D photography applications likeperspective change, simulated aperture, and object segmentation, cancome "for free" for a significant fraction of still photographsunder reasonable conditions.
Real-time Model-based Articulated Object Pose Detection and Tracking with Variable Rigidity Constraints
Authors: Karl Pauwels, Leonardo Rubio, Eduardo Ros
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: A novel model-based approach is introduced for real-time detection and tracking of the pose of general articulated objects. A variety of dense motion and depth cues are integrated into a novel articulated Iterative Closest Point approach. The proposed method can independently track the six-degrees-of-freedom pose of over a hundred of rigid parts in real-time while, at the same time, imposing articulation constraints on the relative motion of different parts. We propose a novel rigidization framework for optimally handling unobservable parts during tracking. This involves rigidly attaching the minimal amount of unseen parts to the rest of the structure in order to most effectively use the currently available knowledge. We show how this framework can be used also for detection rather than tracking which allows for automatic system initialization and for incorporating pose estimates obtained from independent object part detectors. Improved performance over alternative solutions is demonstrated on real-world sequences.
Occluding Contours for Multi-View Stereo
Authors: Qi Shan, Brian Curless, Yasutaka Furukawa, Carlos Hernandez, Steven M. Seitz
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: This paper leverages occluding contours (aka "internal silhouettes") to improve the performance of multi-view stereo methods. The contributions are 1) a new technique to identify free-space regions arising from occluding contours, and 2) a new approach for incorporating the resulting free-space constraints into Poisson surface reconstruction. The proposed approach outperforms state of the art MVS techniques for challenging Internet datasets, yielding dramatic quality improvements both around object contours and in surface detail.
Aerial Reconstructions via Probabilistic Data Fusion
Authors: Randi Cabezas, Oren Freifeld, Guy Rosman, John W. Fisher III
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We propose an integrated probabilistic model for multi-modal fusion of aerial imagery, LiDAR data, and (optional) GPS measurements. The model allows for analysis and dense reconstruction (in terms of both geometry and appearance) of large 3D scenes. An advantage of the approach is that it explicitly models uncertainty and allows for missing data. As compared with image-based methods, dense reconstructions of complex urban scenes are feasible with fewer observations. Moreover, the proposed model allows one to estimate absolute scale and orientation and reason about other aspects of the scene, e.g., detection of moving objects. As formulated, the model lends itself to massively-parallel computing. We exploit this in an efficient inference scheme that utilizes both general purpose and domain-specific hardware components. We demonstrate results on large-scale reconstruction of urban terrain from LiDAR and aerial photography data.
3D Modeling from Wide Baseline Range Scans using Contour Coherence
Authors: Ruizhe Wang, Jongmoo Choi, Gerard Medioni
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Registering 2 or more range scans is a fundamental problem, with application to 3D modeling. While this problem is well addressed by existing techniques such as ICP when the views overlap significantly at a good initialization, no satisfactory solution exists for wide baseline registration. We propose here a novel approach which leverages contour coherence and allows us to align two wide baseline range scans with limited overlap from a poor initialization. Inspired by ICP, we maximize the contour coherence by building robust corresponding pairs on apparent contours and minimizing their distances in an iterative fashion. We use the contour coherence under a multi-view rigid registration framework, and this enables the reconstruction of accurate and complete 3D models from as few as 4 frames. We further extend it to handle articulations, and this allows us to model articulated objects such as human body. Experimental results on both synthetic and real data demonstrate the effectiveness and robustness of our contour coherence based registration approach to wide baseline range scans, and to 3D modeling.
Ground Plane Estimation using a Hidden Markov Model
Authors: Ralf Dragon, Luc Van Gool
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We focus on the problem of estimating the ground plane orientation and location in monocular video sequences from a moving observer. Our only assumptions are that the 3D ego motion t and the ground plane normal n are orthogonal, and that n and t are smooth over time. We formulate the problem as a state-continuous Hidden Markov Model (HMM) where the hidden state contains t and n and may be estimated by sampling and decomposing homographies. We show that using blocked Gibbs sampling, we can infer the hidden state with high robustness towards outliers, drifting trajectories, rolling shutter and an imprecise intrinsic calibration. Since our approach does not need any initial orientation prior, it works for arbitrary camera orientations in which the ground is visible.
Orientation Robust Text Line Detection in Natural Images
Authors: Le Kang, Yi Li, David Doermann
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: In this paper, higher-order correlation clustering (HOCC) is used for text line detection in natural images. We treat text line detection as a graph partitioning problem, where each vertex is represented by a Maximally Stable Extremal Region (MSER). First, weak hypothesises are proposed by coarsely grouping MSERs based on their spatial alignment and appearance consistency. Then, higher-order correlation clustering (HOCC) is used to partition the MSERs into text line candidates, using the hypotheses as soft constraints to enforce long range interactions. We further propose a regularization method to solve the Semidefinite Programming problem in the inference. Finally we use a simple texton-based texture classifier to filter out the non-text areas. This framework allows us to naturally handle multiple orientations, languages and fonts. Experiments show that our approach achieves competitive performance compared to the state of the art.
Strokelets: A Learned Multi-Scale Representation for Scene Text Recognition
Authors: Cong Yao, Xiang Bai, Baoguang Shi, Wenyu Liu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: Driven by the wide range of applications, scene text detection and recognition have become active research topics in computer vision. Though extensively studied, localizing and reading text in uncontrolled environments remain extremely challenging, due to various interference factors. In this paper, we propose a novel multi-scale representation for scene text recognition. This representation consists of a set of detectable primitives, termed as strokelets, which capture the essential substructures of characters at different granularities. Strokelets possess four distinctive advantages: (1) Usability: automatically learned from bounding box labels; (2) Robustness: insensitive to interference factors; (3) Generality: applicable to variant languages; and (4) Expressivity: effective at describing characters. Extensive experiments on standard benchmarks verify the advantages of strokelets and demonstrate the effectiveness of the proposed algorithm for text recognition.
Region-based Discriminative Feature Pooling for Scene Text Recognition
Authors: Chen-Yu Lee, Anurag Bhardwaj, Wei Di, Vignesh Jagadeesh, Robinson Piramuthu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We present a new feature representation method for scene text recognition problem, particularly focusing on improving scene character recognition. Many existing methods rely on Histogram of Oriented Gradient (HOG) or part-based models, which do not span the feature space well for characters in natural scene images, especially given large variation in fonts with cluttered backgrounds. In this work, we propose a discriminative feature pooling method that automatically learns the most informative sub-regions of each scene character within a multi-class classification framework, whereas each sub-region seamlessly integrates a set of low-level image features through integral images. The proposed feature representation is compact, computationally efficient, and able to effectively model distinctive spatial structures of each individual character class. Extensive experiments conducted on challenging datasets (Chars74K, ICDAR'03, ICDAR'11, SVT) show that our method significantly outperforms existing methods on scene character classification and scene text recognition tasks.
Fast and Exact: ADMM-Based Discriminative Shape Segmentation with Loopy Part Models
Authors: Haithem Boussaid, Iasonas Kokkinos
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: In this work we use loopy part models to  segment ensembles of organs in medical images.  Each organ's shape is represented as a cyclic graph, while shape consistency is enforced through  inter-shape connections.  Our contributions are two-fold: firstly, we use an efficient decomposition-coordination algorithm to solve the resulting optimization problems: we decompose the model's graph into a set of open, chain-structured, graphs each of which is efficiently optimized using Dynamic Programming with Generalized Distance Transforms. We use the  Alternating Direction Method of Multipliers (ADMM) to fix the potential inconsistencies of the individual solutions and show that ADMM yields substantially faster convergence than plain  Dual Decomposition-based methods.    Secondly, we  employ structured prediction to encompass loss functions that better reflect the performance criteria used in medical image segmentation. By using the  mean contour distance (MCD)  as a structured loss during training, we obtain clear  test-time performance gains.   We demonstrate the merits of exact and efficient inference with rich, structured models in a large  X-Ray  image segmentation benchmark, where we obtain systematic improvements over the current state-of-the-art.
Pseudoconvex Proximal Splitting for L-infty Problems in Multiview Geometry
Authors: Anders Eriksson, Mats Isaksson
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: In this paper we study optimization methods for minimizing large-scale  pseudoconvex L_infinity problems in multiview geometry. We present a novel algorithm for solving this class of problem based  on proximal splitting methods. We provide a brief derivation of the  proposed method along with a general convergence analysis. The resulting meta-algorithm requires very little effort in terms of implementation and instead makes use of existing  advanced solvers for non-linear optimization. Preliminary experiments on a number of real image datasets indicate that the proposed method experimentally matches or outperforms current state-of-the-art solvers for this class of problems.
A Convex Relaxation of the Ambrosio--Tortorelli Elliptic Functionals for the Mumford-Shah Functional
Authors: Youngwook Kee, Junmo Kim
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: In this paper, we revisit the phase-field approximation of Ambrosio and Tortorelli for the Mumford--Shah functional. We then propose a convex relaxation for it to attempt to compute globally optimal solutions rather than solving the nonconvex functional directly, which is the main contribution of this paper. Inspired by McCormick's seminal work on factorable nonconvex problems, we split a nonconvex product term that appears in the Ambrosio--Tortorelli elliptic functionals in a way that a typical alternating gradient method guarantees a globally optimal solution without completely removing coupling effects. Furthermore, not only do we provide a fruitful analysis of the proposed relaxation but also demonstrate the capacity of our relaxation in numerous experiments that show convincing results compared to a naive extension of the McCormick relaxation and its quadratic variant. Indeed, we believe the proposed relaxation and the idea behind would open up a possibility for convexifying a new class of functions in the context of energy minimization for computer vision.
Sequential Convex Relaxation for Mutual Information-Based Unsupervised Figure-Ground Segmentation
Authors: Youngwook Kee, Mohamed Souiai, Daniel Cremers, Junmo Kim
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: We propose an optimization algorithm for mutual-information-based unsupervised figure-ground separation. The algorithm jointly estimates the color distributions of the foreground and background, and separates them based on their mutual information with geometric regularity. To this end, we revisit the notion of mutual information and reformulate it in terms of the photometric variable and the indicator function; and propose a sequential convex optimization strategy for solving the nonconvex optimization problem that arises. By minimizing a sequence of convex sub-problems for the mutual-information-based nonconvex energy, we efficiently attain high quality solutions for challenging unsupervised figure-ground segmentation problems. We demonstrate the capacity of our approach in numerous experiments that show convincing fully unsupervised figure-ground separation, in terms of both segmentation quality and robustness to initialization.
Decorrelated Vectorial Total Variation
Authors: Shunsuke Ono, Isao Yamada
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (Main Conference Track), 2014

Abstract: This paper proposes a new vectorial total variation prior (VTV) for color images. Different from existing VTVs, our VTV, named the decorrelated vectorial total variation prior (D-VTV), measures the discrete gradients of the luminance component and that of the chrominance one in a separated manner, which significantly reduces undesirable uneven color effects. Moreover, a higher-order generalization of the D-VTV, which we call the decorrelated vectorial total generalized variation prior (D-VTGV), is also developed for avoiding the staircasing effect that accompanies the use of VTVs. A noteworthy property of the D-VT(G)V is that it enables us to efficiently minimize objective functions involving it by a primal-dual splitting method. Experimental results illustrate their utility.