Abstract. In this paper we describe a flexible approach for determining the relative orientation of the camera with respect to the scene. The main premise of the approach is the fact that in man-made environments, the majority of lines is aligned with the principal orthogonal directions of the world coordinate frame. We exploit this observation towards efficient detection and estimation of vanishing points, which provide strong constraints on camera parameters and relative orientation of the camera with respect to the scene. By combining efficient image processing techniques in the line detection and initialization stage we demonstrate that simultaneous grouping and estimation of vanishing directions can be achieved in the absence of internal parameters of the camera. Constraints between vanishing points are then used for partial calibration and relative rotation estimation. The algorithm has been tested in a variety of indoors and outdoors scenes and its efficiency and automation makes it amenable for implementation on robotic platforms. Key words: Vanishing point estimation, relative orientation, calibration using vanishing points, vision guided mobile and aerial robots. 1

This paper presents a linear algorithm for the simultaneous computation of 3D points and camera positions from multiple perspective views, based on having four points on a reference plane visible in all views. The reconstruction and camera recovery is achieved, in a single step, by finding the null-space of a matrix using singular value decomposition. Unlike factorization algorithms, the presented algorithm does not require all points to be visible in all views. By simultaneously reconstructing points and views the numerically stabilizing effect of having wide spread cameras with large mutual baselines is exploited. Experimental results are presented for both finite and infinite reference planes. An especially interesting application of this method is the reconstruction of architectural scenes with the reference plane taken as the plane at infinity which is visible via three orthogonal vanishing points. This is demonstrated by reconstructing the outside and inside (courtyard) of a building on the basis of 35 views in one single SVD.

In this paper, we consider the problem of recovering the spatial layout of indoor scenes from monocular images. The presence of clutter is a major problem for existing singleview 3D reconstruction algorithms, most of which rely on finding the ground-wall boundary. In most rooms, this boundary is partially or entirely occluded. We gain robustness to clutter by modeling the global room space with a parameteric 3D “box ” and by iteratively localizing clutter and refitting the box. To fit the box, we introduce a structured learning algorithm that chooses the set of parameters to minimize error, based on global perspective cues. On a dataset of 308 images, we demonstrate the ability of our algorithm to recover spatial layout in cluttered rooms and show several examples of estimated free space. 1.

We study the problem of generating plausible interpretations of a scene from a collection of line segments automatically extracted from a single indoor image. We show that we can recognize the three dimensional structure of the interior of a building, even in the presence of occluding objects. Several physically valid structure hypotheses are proposed by geometric reasoning and verified to find the best fitting model to line segments, which is then converted to a full 3D model. Our experiments demonstrate that our structure recovery from line segments is comparable with methods using full image appearance. Our approach shows how a set of rules describing geometric constraints between groups of segments can be used to prune scene interpretation hypotheses and to generate the most plausible interpretation. Figure 1. Line segments. Can you recognize the building structure? Can you find doors? 1.

Figure 1: Our interactive image-based modeling system provides an intuitive sketch-based interface for reconstructing a photorealistic textured piecewise planar 3D model of a building or architectural scene from an unordered collection of photographs. We present an interactive system for generating photorealistic, textured, piecewise-planar 3D models of architectural structures and urban scenes from unordered sets of photographs. To reconstruct 3D geometry in our system, the user draws outlines overlaid on 2D photographs. The 3D structure is then automatically computed by combining the 2D interaction with the multi-view geometric information recovered by performing structure from motion analysis on the input photographs. We utilize vanishing point constraints at multiple stages during the reconstruction, which is particularly useful for architectural scenes where parallel lines are abundant. Our approach enables us to accurately model polygonal faces from 2D interactions in a single image. Our system also supports useful operations such as edge snapping and extrusions. Seamless texture maps are automatically generated by combining multiple input photographs using graph cut optimization and Poisson blending. The user can add brush strokes as hints during the texture generation stage to remove artifacts caused by unmodeled geometric structures. We build models for a variety of architectural scenes from collections of up to about a hundred photographs. CR Categories: I.3.7 [Computer Graphics]: Three-dimensional graphics and realism, image-based modeling, texture mapping—

In this paper model-based reconstruction methods are applied to the detailed reconstruction of buildings from close-range images. The 3D points obtained through image matching are segmented into a coarse polyhedral model with a robust regression algorithm, then the geometry of this model is refined with predefined shape templates in order to automatically recover a CAD-like model of the building surface. Reprojection of the 3D shape templates is used to optimally fit their parameters to the image information. Throughout the paper the investigated methods are demonstrated on real datasets. 1.

Abstract not found

A method for the fronto-parallel recovery of paragraphs of text under full perspective transformation is presented. The horizontal vanishing point of the text plane is found using an extension of 2D projection profiles. This allows the accurate segmentation of the lines of text. Analysis of the lines will then reveal the style of justification of the paragraph, and provide an estimate of the vertical vanishing point of the plane. The text is finally recovered to a fronto-parallel view suitable for OCR or other higher-level recognition.

The task of recovering three-dimensional (3-D) geometry from two-dimensional views of a scene is called 3-D reconstruction. It is an extremely active research area in computer vision. There is a large body of 3-D reconstruction algorithms available in the literature. These algorithms are often designed to provide different tradeoffs between speed, accuracy, and practicality. In addition, even the output of various algorithms can be quite different. For example, some algorithms only produce a sparse 3-D reconstruction while others are able to output a dense reconstruction. The selection of the appropriate 3-D reconstruction algorithm relies heavily on the intended application as well as the available resources. The goal of this paper is to review some of the commonly used motion-parallax-based 3-D reconstruction techniques and make clear the assumptions under which they are designed. To do so efficiently, we classify the reviewed reconstruction algorithms into two large categories depending on whether a prior calibration

Abstract. We address the problem of understanding an indoor scene from a single image in terms of recovering the layouts of the faces (floor, ceiling, walls) and furniture. A major challenge of this task arises from the fact that most indoor scenes are cluttered by furniture and decorations, whose appearances vary drastically across scenes, and can hardly be modeled (or even hand-labeled) consistently. In this paper we tackle this problem by introducing latent variables to account for clutters, so that the observed image is jointly explained by the face and clutter layouts. Model parameters are learned in the maximum margin formulation, which is constrained by extra prior energy terms that define the role of the latent variables. Our approach enables taking into account and inferring indoor clutter layouts without hand-labeling of the clutters in the training set. Yet it outperforms the state-of-the-art method of Hedau et al. [4] that requires clutter labels. 1