Acquiring 3D geometry of real world objects has various applications in 3D digitization, such as navigation and content generation in virtual environments. Image remains one of the most popular media for such visual tasks due to its simplicity of acquisition. Traditional image-based 3D reconstruction approaches heavily exploit point-to-point correspondence among multiple images to estimate camera motion and 3D geometry. Establishing point-to-point correspondence lies at the center of the 3D reconstruction pipeline, which however is easily prone to errors. In this paper, we propose an optimization framework which traces image points using a novel structure-guided dynamic tracking algorithm and estimates both the camera motion and a 3D structure model by enforcing a set of planar constraints. The key to our method is a structure model represented as a set of planes and their arrangements. Constraints derived from the structure model is used both in the correspondence establishment stage and the bundle adjustment stage in our reconstruction pipeline. Experiments show that our algorithm can effectively localize structure correspondence across dense image frames while faithfully reconstructing the camera motion and the underlying structured 3D model.
By giving pre-known constraints in perspective projection, we can recover the 3D information from a single image @cite_13 @cite_7 @cite_8 by calculating the normals. But single image has a very limited field of view, and can not deal with the occlusion without additional symmetry assumption @cite_7 @cite_16 .
@cite_16 @cite_13 @cite_7 @cite_8
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Recovering 3D geometry from a single view of an object is an important and challenging problem in computer vision. Previous methods mainly focus on one specific class of objects without large topological changes, such as cars, faces, or human bodies. In this paper, we propose a novel single view reconstruction algorithm for symmetric piece-wise planar objects that are not restricted to some object classes. Symmetry is ubiquitous in manmade and natural objects and provides rich information for 3D reconstruction. Given a single view of a symmetric piecewise planar object, we first find out all the symmetric line pairs. The geometric properties of symmetric objects are used to narrow down the searching space. Then, based on the symmetric lines, a depth map is recovered through a Markov random field. Experimental results show that our algorithm can efficiently recover the 3D shapes of different objects with significant topological variations. We present a method to recover a 3D texture-mapped architecture model from a single image. Both single image based modeling and architecture modeling are challenging problems. We handle these difficulties by employing constraints derived from shape symmetries, which are prevalent in architecture. We first present a novel algorithm to calibrate the camera from a single image by exploiting symmetry. Then a set of 3D points is recovered according to the calibration and the underlying symmetry. With these reconstructed points, the user interactively marks out components of the architecture structure, whose shapes and positions are automatically determined according to the 3D points. Lastly, we texture the 3D model according to the input image, and we enhance the texture quality at those foreshortened and occluded regions according to their symmetric counterparts. The modeling process requires only a few minutes interaction. Multiple examples are provided to demonstrate the presented method. We present an approach for 3D reconstruction of objects from a single image. Obviously, constraints on the 3D structure are needed to perform this task. Our approach is based on user-provided coplanarity, perpendicularity and parallelism constraints. These are used to calibrate the image and perform 3D reconstruction. The method is described in detail and results are provided.