pwc-archive/papers-with-abstracts huggingface dataset:
Complex Non-Rigid Motion 3D Reconstruction by Union of Subspaces
Authors: Yingying Zhu, Dong Huang, Fernando de la Torre, Simon Lucey
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Reconstruction

The task of estimating complex non-rigid 3D motion through a monocular camera is of increasing interest to the wider scientific community. Assuming one has the 2D point tracks of the non-rigid object in question, the vision community refers to this problem as Non-Rigid Structure from Motion (NRSfM). In this paper we make two contributions. First, we demonstrate empirically that the current state of the art approach to NRSfM (i.e. Dai et al. [5]) exhibits poor reconstruction performance on complex motion (i.e motions involving a sequence of primitive actions such as walk, sit and stand involving a human object). Second, we propose that this limitation can be circumvented by modeling complex motion as a union of subspaces. This does not naturally occur in Dai et al.'s approach which instead makes a less compact summation of subspaces assumption. Experiments on both synthetic and real videos illustrate the benefits of our approach for the complex nonrigid motion analysis.
A Procrustean Markov Process for Non-Rigid Structure Recovery
Authors: Minsik Lee, Chong-Ho Choi, Songhwai Oh
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

Recovering a non-rigid 3D structure from a series of 2D observations is still a difficult problem to solve accurately. Many constraints have been proposed to facilitate the recovery, and one of the most successful constraints is smoothness due to the fact that most real-world objects change continuously. However, many existing methods require to determine the degree of smoothness beforehand, which is not viable in practical situations. In this paper, we propose a new probabilistic model that incorporates the smoothness constraint without requiring any prior knowledge. Our approach regards the sequence of 3D shapes as a simple stationary Markov process with Procrustes alignment, whose parameters are learned during the fitting process. The Markov process is assumed to be stationary because deformation is finite and recurrent in general, and the 3D shapes are assumed to be Procrustes aligned in order to discriminate deformation from motion. The proposed method outperforms the state-of-the-art methods, even though the computation time is rather moderate compared to the other existing methods.
Good Vibrations: A Modal Analysis Approach for Sequential Non-Rigid Structure from Motion
Authors: Antonio Agudo, Lourdes Agapito, Begona Calvo, Jose M. M. Montiel
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

We propose an online solution to non-rigid structure from motion that performs camera pose and 3D shape estimation of highly deformable surfaces on a frame-by-frame basis. Our method models non-rigid deformations as a linear combination of some mode shapes obtained using modal analysis from continuum mechanics. The shape is first discretized into linear elastic triangles, modelled by means of finite elements, which are used to pose the force balance equations for an undamped free vibrations model. The shape basis computation comes down to solving an eigenvalue problem, without the requirement of a learning step. The camera pose and time varying weights that define the shape at each frame are then estimated on the fly, in an online fashion, using bundle adjustment over a sliding window of image frames. The result is a low computational cost method that can run sequentially in real-time. We show experimental results on synthetic sequences with ground truth 3D data and real videos for different scenarios ranging from sparse to dense scenes. Our system exhibits a good trade-off between accuracy and computational budget, it can handle missing data and performs favourably compared to competing methods.
Robust Scale Estimation in Real-Time Monocular SFM for Autonomous Driving
Authors: Shiyu Song, Manmohan Chandraker
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Autonomous Driving, Object, object-detection, Object Detection, Object Localization

Scale drift is a crucial challenge for monocular autonomous driving to emulate the performance of stereo. This paper presents a real-time monocular SFM system that corrects for scale drift using a novel cue combination framework for ground plane estimation, yielding accuracy comparable to stereo over long driving sequences. Our ground plane estimation uses multiple cues like sparse features, dense inter-frame stereo and (when applicable) object detection. A data-driven mechanism is proposed to learn models from training data that relate observation covariances for each cue to error behavior of its underlying variables. During testing, this allows per-frame adaptation of observation covariances based on relative confidences inferred from visual data. Our framework significantly boosts not only the accuracy of monocular self-localization, but also that of applications like object localization that rely on the ground plane. Experiments on the KITTI dataset demonstrate the accuracy of our ground plane estimation, monocular SFM and object localization relative to ground truth, with detailed comparisons to prior art.
On the Quotient Representation for the Essential Manifold
Authors: Roberto Tron, Kostas Daniilidis
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

The essential matrix, which encodes the epipolar constraint between points in two projective views, is a cornerstone of modern computer vision. Previous works have proposed different characterizations of the space of essential matrices as a Riemannian manifold. However, they either do not consider the symmetric role played by the two views, or do not fully take into account the geometric peculiarities of the epipolar constraint. We address these limitations with a characterization as a quotient manifold which can be easily interpreted in terms of camera poses. While our main focus in on theoretical aspects, we include experiments in pose averaging, and show that the proposed formulation produces a meaningful distance between essential matrices.
Efficient High-Resolution Stereo Matching using Local Plane Sweeps
Authors: Sudipta N. Sinha, Daniel Scharstein, Richard Szeliski
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Clustering, global-optimization, Stereo Matching, Stereo Matching Hand, Vocal Bursts Intensity Prediction

We present a stereo algorithm designed for speed and efficiency that uses local slanted plane sweeps to propose disparity hypotheses for a semi-global matching algorithm. Our local plane hypotheses are derived from initial sparse feature correspondences followed by an iterative clustering step. Local plane sweeps are then performed around each slanted plane to produce out-of-plane parallax and matching-cost estimates. A final global optimization stage, implemented using semi-global matching, assigns each pixel to one of the local plane hypotheses. By only exploring a small fraction of the whole disparity space volume, our technique achieves significant speedups over previous algorithms and achieves state-of-the-art accuracy on high-resolution stereo pairs of up to 19 megapixels.
Asymmetrical Gauss Mixture Models for Point Sets Matching
Authors: Wenbing Tao, Kun Sun
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

The probabilistic methods based on Symmetrical Gauss Mixture Model (SGMM) have achieved great success in point sets registration, but are seldom used to find the correspondences between two images due to the complexity of the non-rigid transformation and too many outliers. In this paper we propose an Asymmetrical GMM (AGMM) for point sets matching between a pair of images. Different from the previous SGMM, the AGMM gives each Gauss component a different weight which is related to the feature similarity between the data point and model point, which leads to two effective algorithms: the Single Gauss Model for Mismatch Rejection (SGMR) algorithm and the AGMM algorithm for point sets matching. The SGMR algorithm iteratively filters mismatches by estimating a non-rigid transformation between two images based on the spatial coherence of point sets. The AGMM algorithm combines the feature information with position information of the SIFT feature points extracted from the images to achieve point sets matching so that much more correct correspondences with high precision can be found. A number of comparison and evaluation experiments reveal the excellent performance of the proposed SGMR algorithm and AGMM algorithm.
Fast and Reliable Two-View Translation Estimation
Authors: Johan Fredriksson, Olof Enqvist, Fredrik Kahl
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Motion Estimation, Translation, Vocal Bursts Valence Prediction

It has long been recognized that one of the fundamental difficulties in theestimation of two-view epipolar geometry is the capability of handling outliers. In this paper, we develop a fast and tractable algorithm that maximizes the number of inliers under the assumption of a purely translating camera. Compared to classical random sampling methods, our approach is guaranteed to compute the optimal solution of a cost function based on reprojection errors and it has better time complexity. The performance is in fact independent of the inlier/outlier ratio of the data.This opens up for a more reliable approach to robust ego-motion estimation.  Our basic translation estimator can be embedded into a system that computes the full camera rotation. We demonstrate the applicability in several difficult settings with large amounts of outliers. It turns out to be particularly well-suited for small rotations and rotations around a known axis (which is the case for cellular phones where the gravitation axis can be measured). Experimental results show that compared to standard ransac methods based on minimal solvers, ouralgorithm produces more accurate estimates in the presence of large outlier ratios.
Graph Cut based Continuous Stereo Matching using Locally Shared Labels
Authors: Tatsunori Taniai, Yasuyuki Matsushita, Takeshi Naemura
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Disparity Estimation, Stereo Matching, Stereo Matching Hand

We present an accurate and efficient stereo matching method using locally shared labels, a new labeling scheme that enables spatial propagation in MRF inference using graph cuts. They give each pixel and region a set of candidate disparity labels, which are randomly initialized, spatially propagated, and refined for continuous disparity estimation. We cast the selection and propagation of locallydefined disparity labels as fusion-based energy minimization. The joint use of graph cuts and locally shared labels has advantages over previous approaches based on fusion moves or belief propagation; it produces submodular moves deriving a subproblem optimality; enables powerful randomized search; helps to find good smooth, locally planar disparity maps, which are reasonable for natural scenes; allows parallel computation of both unary and pairwise costs. Our method is evaluated using the Middlebury stereo benchmark and achieves first place in sub-pixel accuracy.
Learning to Detect Ground Control Points for Improving the Accuracy of Stereo Matching
Authors: Aristotle Spyropoulos, Nikos Komodakis, Philippos Mordohai
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Stereo Matching, Stereo Matching Hand

While machine learning has been instrumental to the ongoing progress in most areas of computer vision, it has not been applied to the problem of stereo matching with similar frequency or success. We present a supervised learning approach for predicting the correctness of stereo matches based on a random forest and a set of features that capture various forms of information about each pixel. We show highly competitive results in predicting the correctness of matches and in confidence estimation, which allows us to rank pixels according to the reliability of their assigned disparities. Moreover, we show how these confidence values can be used to improve the accuracy of disparity maps by integrating them with an MRF-based stereo algorithm. This is an important distinction from current literature that has mainly focused on sparsification by removing potentially erroneous disparities to generate quasi-dense disparity maps.
Decorrelating Semantic Visual Attributes by Resisting the Urge to Share
Authors: Dinesh Jayaraman, Fei Sha, Kristen Grauman
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Attribute, Multi-Task Learning

Existing methods to learn visual attributes are prone to learning the wrong thing---namely, properties that are correlated with the attribute of interest among training samples.  Yet, many proposed applications of attributes rely on being able to learn the correct semantic concept corresponding to each attribute.  We propose to resolve such confusions by jointly learning decorrelated, discriminative attribute models.  Leveraging side information about semantic relatedness, we develop a multi-task learning approach that uses structured sparsity to encourage feature competition among unrelated attributes and feature sharing among related attributes.  On three challenging datasets, we show that accounting for structure in the visual attribute space is key to learning attribute models that preserve semantics, yielding improved generalizability that helps in the recognition and discovery of unseen object categories.
Learning Scalable Discriminative Dictionary with Sample Relatedness
Authors: Jiashi Feng, Stefanie Jegelka, Shuicheng Yan, Trevor Darrell
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Dictionary Learning, Image Retrieval, Object, Object Recognition, Retrieval

Attributes are widely used as mid-level descriptors of object properties in object recognition and retrieval. Mostly, such attributes are manually pre-defined based on domain knowledge, and their number is fixed. However, pre-defined attributes may fail to adapt to the properties  of the data at hand, may not necessarily be discriminative, and/or may not generalize well. In this work, we propose a dictionary learning framework that flexibly adapts to the complexity of the given data set and reliably discovers the inherent discriminative middle-level binary features in the data. We use sample relatedness information to improve the generalization of the learned dictionary. We demonstrate that our framework is applicable to both object recognition and complex image retrieval tasks even with few training examples. Moreover, the learned dictionary also help classify novel object categories. Experimental results on the Animals with Attributes, ILSVRC2010 and PASCAL VOC2007 datasets indicate that using relatedness information leads to significant performance gains over established baselines.
Iterated Second-Order Label Sensitive Pooling for 3D Human Pose Estimation
Authors: Catalin Ionescu, Joao Carreira, Cristian Sminchisescu
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Human Pose Estimation, Pose Estimation

Recently, the emergence of Kinect systems has demonstrated the benefits of predicting an intermediate body part labeling for 3D human pose estimation, in conjunction with RGB-D imagery. The availability of depth information plays a critical role, so an important question is whether a similar representation can be developed with sufficient robustness in order to estimate 3D pose from RGB images. This paper provides evidence for a positive answer, by leveraging (a) 2D human body part labeling in images, (b) second-order label-sensitive pooling over dynamically computed regions resulting from a hierarchical decomposition of the body, and (c) iterative structured-output modeling to contextualize the process based on 3D pose estimates. For robustness and generalization, we take advantage of a recent large-scale 3D human motion capture dataset, Human3.6M [18] that also has human body part labeling annotations available with images. We provide extensive experimental studies where alternative intermediate representations are compared and report a substantial 33% error reduction over competitive discriminative baselines that regress 3D human pose against global HOG features.
3D Pictorial Structures for Multiple Human Pose Estimation
Authors: Vasileios Belagiannis, Sikandar Amin, Mykhaylo Andriluka, Bernt Schiele, Nassir Navab, Slobodan Ilic
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Human Pose Estimation, 3D Multi-Person Pose Estimation, 3D Pose Estimation, Pose Estimation

In this work, we address the problem of 3D pose estimation of multiple humans from multiple views. This is a more challenging problem than single human 3D pose estimation due to the much larger state space, partial occlusions as well as across view ambiguities when not knowing the identity of the humans in advance. To address these problems, we first create a reduced state space by triangulation of corresponding body joints obtained from part detectors  in pairs of camera views. In order to resolve the ambiguities of wrong and mixed body parts of multiple humans after triangulation and also those coming from false positive body part detections, we introduce a novel 3D pictorial structures (3DPS) model. Our model infers 3D human body configurations from our reduced state space. The 3DPS model is generic and applicable to both single and multiple human pose estimation.  In order to compare to the state-of-the art, we first evaluate our method on single human 3D pose estimation on HumanEva-I [22] and  KTH Multiview Football Dataset II [8] datasets. Then, we introduce and evaluate our method on two datasets for multiple human 3D pose estimation.
Learning Euclidean-to-Riemannian Metric for Point-to-Set Classification
Authors: Zhiwu Huang, Ruiping Wang, Shiguang Shan, Xilin Chen
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Classification, General Classification, Metric Learning

In this paper, we focus on the problem of point-to-set classification, where single points are matched against sets of correlated points. Since the points commonly lie in Euclidean space while the sets are typically modeled as elements on Riemannian manifold, they can be treated as Euclidean points and Riemannian points respectively. To learn a metric between the heterogeneous points, we propose a novel Euclidean-to-Riemannian metric learning framework. Specifically, by exploiting typical Riemannian metrics, the Riemannian manifold is first embedded into a high dimensional Hilbert space to reduce the gaps between the heterogeneous spaces and meanwhile respect the Riemannian geometry of the manifold. The final distance metric is then learned by pursuing multiple transformations from the Hilbert space and the original Euclidean space (or its corresponding Hilbert space) to a common Euclidean subspace, where classical Euclidean distances of transformed heterogeneous points can be measured. Extensive experiments clearly demonstrate the superiority of our proposed approach over the state-of-the-art methods.
Face Alignment at 3000 FPS via Regressing Local Binary Features
Authors: Shaoqing Ren, Xudong Cao, Yichen Wei, Jian Sun
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Face Alignment, regression

This paper presents a highly efficient, very accurate regression approach for face alignment. Our approach has two novel components: a set of local binary features, and a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently. The obtained local binary features are used to jointly learn a linear regression for the final output. Our approach achieves the state-of-the-art results when tested on the current most challenging benchmarks. Furthermore, because extracting and regressing local binary features is computationally very cheap, our system is much faster than previous methods. It achieves over 3,000 fps on a desktop or 300 fps on a mobile phone for locating a few dozens of landmarks.
A Compact and Discriminative Face Track Descriptor
Authors: Omkar M. Parkhi, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Binarization, Dimensionality Reduction, Face Recognition, Face Verification, General Classification, Quantization

Our goal is to learn a compact, discriminative vector representation of a face track, suitable for the face recognition tasks of verification and  classification. To this end, we propose a novel face track descriptor, based on the Fisher Vector representation, and demonstrate that it has a  number of favourable properties. First, the descriptor is suitable for tracks of both frontal and profile faces, and is insensitive to their pose. Second, the descriptor is compact due to discriminative dimensionality reduction, and it can be further compressed using binarization. Third, the descriptor can be computed quickly (using hard quantization) and its compact size and fast computation render it very suitable  for large scale visual repositories. Finally, the descriptor demonstrates good generalization when trained on one dataset and tested on another,  reflecting its tolerance to the dataset bias. In the experiments we show that the descriptor exceeds the state of the art on both  face verification task (YouTube Faces without outside training data, and INRIA-Buffy benchmarks), and face classification task (using the Oxford-Buffy dataset).
Filter Forests for Learning Data-Dependent Convolutional Kernels
Authors: Sean Ryan Fanello, Cem Keskin, Pushmeet Kohli, Shahram Izadi, Jamie Shotton, Antonio Criminisi, Ugo Pattacini, Tim Paek
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Denoising

We propose 'filter forests' (FF), an efficient new discriminative approach for predicting continuous variables given a signal and its context. FF can be used for general signal restoration tasks that can be tackled via convolutional filtering, where it attempts to learn the optimal filtering kernels to be applied to each data point. The model can learn both the size of the kernel and its values, conditioned on the observation and its spatial or temporal context. We show that FF compares favorably to both Markov random field based and recently proposed regression forest based approaches for labeling problems in terms of efficiency and accuracy. In particular, we demonstrate how FF can be used to learn optimal denoising filters for natural images as well as for other tasks such as depth image refinement, and 1D signal magnitude estimation. Numerous experiments and quantitative comparisons show that FFs achieve accuracy at par or superior to recent state of the art techniques, while being several orders of magnitude faster.