We present a novel view on the indoor visual localization problem, where we avoid the use of interest points and associated descriptors, which are the basic building blocks of most standard methods. Instead, localization is cast as an alignment problem of the edges of the query image to a 3D model consisting of line segments. The proposed strategy is effective in low-textured indoor environments and in very wide baseline setups as it overcomes the dependency of image descriptors on textures, as well as their limited invariance to view point changes. The basic features of our method, which are prevalent indoors, are line segments. As we will show, they allow for defining an efficient Chamfer distance-based aligning cost, computed through integral contour images, incorporated into a first-best-search strategy. Experiments confirm the effectiveness of the method in terms of both, accuracy and computational complexity.

We present a novel SfM pipeline which estimates motion and wiry 3D structure from imaged line segments across multiple views. Though the position of lines can be determined more accurately than point features, the issue of detecting stable endpoints diverted most research focus away to point-based methods. In our approach, we tackle this problem by utilizing relaxed constraints on the endpoint positions both during matching as well as in the Bundle Adjustment stage. Furthermore, we gain efficiency in estimating trifocal image relations by decoupling rotation and translation. To this end, a novel linear solver for relative translation estimation given rotations from five line correspondences in three views is introduced. Experiments on long sequences show that our line-based SfM pipeline advantageously complements point-based methods, giving more meaningful 3D representation for indoor scenarios.

We present a novel minimal solution for the uncalibrated absolute pose problem given one vanishing point. The proposed method complements state-of-the-art minimal absolute pose solvers and contributes to the handling camera localization problem in poor textured indoor environments. Those environments almost always exhibit edges aligned with the gravity direction which can be detected in images as a vanishing point. The vanishing point with suitable parametrization of the unknowns allows to cast the problem as a Quartic Eigenvalue Problem, a type of nonlinear problem for which very effective solvers exist. The proposed solution yields a reduction in one of the point correspondences which are required by PnP algorithms. In comparison to the general four point PnP algorithms, our method is numerically more stable and more robust to noise. This is demonstrated on a variety of synthetic and real data in scope of calibrating surveillance and mobile phone cameras.

Radial lens distortion found in real, most notably off-the-shelf medium to wide angle optics can be quite severe. A-priori calibration remedies the problem, but requires access to the camera. Alternative approaches make use of correspondences in multiple images of a moving camera [3], relying on sufficiently overlapping views. Our paper fits in the category of techniques, where straight scene lines are used to determine distortion parameters from a single image. These plumbline methods [2] critically depend on the existence of long edges and get thrown of easily if a significant portion originates from non-linear structures. Here, the use of vanishing points provides an advantage: When a large number of image edges groups w.r.t. to a common vanishing point, it is very likely that they stem from parallel scene lines. Recent methods for vanishing point detection either ignore radial distortion completely, or assume only weak distortion, which is accounted for after vanishing point estimation [1, 4]. If strong distortion is present the effects are detrimental to the extraction of vanishing points and such algorithms are bound to fail. Furthermore, when dealing with radial distortion effects in refinement stages, the error is assessed in the undistorted image. This creates significant bias towards estimates shrinking the undistorted image [5] Contributions: To overcome this limitation, we suggest a simple RANSAC procedure for vanishing point detection by clustering of distorted lines. The proposed technique builds upon: a) Our newly devised closed-form solution for simultaneously estimating a vanishing point and radial distortion from three distorted image lines. b) A consistency measure which avoid bias by quantifying the error of lines w.r.t. a vanishing point in the distorted image. Specifically, our work bases on the division model [3], mapping a distorted image point x = (x,y) to the undistorted point x by

We focus on the detection of orthogonal vanishing points using line segments extracted from a single view, and using these for camera self-calibration. Recent methods view this problem as a two-stage process. Vanishing points are extracted through line segment clustering and subsequently likely orthogonal candidates are selected for calibration. Unfortunately, such an approach is easily distracted by the presence of clutter. Furthermore, geometric constraints imposed by the camera and scene orthogonality are not enforced during detection, leading to inaccurate results which are often inadmissible for calibration. To overcome these limitations, we present a RANSAC-based approach using a minimal solution for estimating three orthogonal vanishing points and focal length from a set of four lines, aligned with either two or three orthogonal directions. In addition, we propose to refine the estimates using an efficient and robust Maximum Likelihood Estimator. Extensive experiments on standard datasets show that our contributions result in significant improvements over the state-of-the-art.

In this paper we address the problem of object detection in cluttered scenes. Local image features and their spatial configuration act as representation of object classes which are learned in a discriminative fashion. Recent contributions in the area of object detection indicate the importance of using geometrical properties for representing object classes. Prompted by this, we devised an approach tailored to control the importance of the features and their spatial alignment. We quantitatively show that modeling the spatial distribution of local features and optimising the influence of both cues significantly boosts object detection performance.

In this paper, we describe a novel algorithm for the detection of grasping points in images of previously unseen objects. A basic building block of our approach is the use of a newly devised descriptor, representing semi-local grasping point shape by the use edge orientation histograms. Combined with boosting, our method learns discriminative grasp point models for new objects from a set of annotated real-world images. The method has been extensively evaluated on challenging images of real scenes, exhibiting largely varying characteristics concerning illumination conditions, scene complexity, and viewpoint. Our experiments show that the method works in a stable manner and that its performance compares favorably to the state-of-the-art.

Indoor and outdoor urban environments posses many regularities which can be efficiently exploited and used for general image parsing tasks. We present a novel approach for detecting rectilinear structures and demonstrate their use for wide baseline stereo matching, planar 3D reconstruction, and computation of geometric context. Assuming a presence of dominant orthogonal vanishing directions, we proceed by formulating the detection of the rectilinear structures as a labeling problem on detected line segments. The line segment labels, respecting the proposed grammar rules, are established as the MAP assignment of the corresponding MRF. The proposed framework allows to detect both full as well as partial rectangles, rectangle-in-rectangle structures, and rectangles sharing edges. The use of detected rectangles is demonstrated in the context of difficult wide baseline matching tasks in the presence of repetitive structures and large appearance changes.

In this paper, we describe the components of a novel algorithm for the extraction of dominant orthogonal planar structures from monocular images taken in indoor environments. The basic building block of our approach is the use of vanishing points and vanishing lines imposed by the frequently observed dominance of three mutually orthogonal vanishing directions in man-made world. Vanishing points are found by an improved approach, taking no assumptions on known internal or external camera parameters. The problem of detecting planar patches is attacked using a probabilistic framework, searching for the maximum a posteriori probability (MAP) in a Markov Random Field (MRF). For this, we propose a novel formulation fusing geometric information obtained from vanishing points and features, such as rectangles and partial rectangles, together with a color-homogeneity criteria imposed by an image over-segmentation. The method was evaluated on a set of images exhibiting largely varying characteristics concerning image quality and scene complexity. Experiments show that the method, despite the variations, works in a stable manner and that its performance compares favorably to the state-of-the-art.