pwc-archive/papers-with-abstracts huggingface dataset:
Local Readjustment for High-Resolution 3D Reconstruction
Authors: Siyu Zhu, Tian Fang, Jianxiong Xiao, Long Quan
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Reconstruction, Vocal Bursts Intensity Prediction

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Reconstruction

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Reconstruction, Image Enhancement, Optical Flow Estimation

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Camera Calibration

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: geo-localization

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Reconstruction, Semantic Segmentation

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Surface Reconstruction

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Clustering, graph partitioning, Line Detection

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Scene Text Detection, Scene Text Recognition, Text Detection

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: General Classification, Multi-class Classification, Scene Text Recognition

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Image Segmentation, Medical Image Segmentation, Semantic Segmentation, Structured Prediction

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Segmentation

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
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

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.