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  • Fredrik Kahl
  • Tenth IEEE International Conference on Computer…
  • 2005
This paper presents a new framework for solving geometric structure and motion problems based on L/sub /spl infin//-norm. Instead of using the common sum-of-squares cost-function, that is, the L/sub /spl infin//-norm, the model-fitting errors are measured using the L/sub /spl infin//-norm. Unlike traditional methods based on L/sub 2/ our framework allows(More)
This paper introduces a new algorithmic technique for solving certain problems in geometric computer vision. The main novelty of the method is a branch-and-bound search over rotation space, which is used in this paper to determine camera orientation. By searching over all possible rotations, problems can be reduced to known fixed-rotation problems for which(More)
This paper presents a new framework for solving geometric structure and motion problems based on L∞-norm. Instead of using the common sum-of-squares cost function, that is, the L2-norm, the model-fitting errors are measured using the L∞-norm. Unlike traditional methods based on L2, our framework allows for efficient computation of global estimates. We show(More)
Assume that we have two perspective images with known intrinsic parameters except for an unknown common focal length. It is a minimally constrained problem to find the relative orientation between the two images given six corresponding points. We present an efficient solution to the problem and show that there are 15 solutions in general (including complex(More)
The estimation of structure and motion from image sequences is one of the most studied problems within computer vision. However, almost all the efforts in this area have dealt with rigid objects. Our surrounding environment is generally not a rigid place, with swaying trees, moving people and rolling waves. Hence, to have structure and motion systems work(More)
The ability to quickly acquire 3D models is an essential capability needed in many disciplines including robotics, computer vision, geodesy, and architecture. In this paper we present a novel method for real-time camera tracking and 3D reconstruction of static indoor environments using an RGB-D sensor. We show that by representing the geometry with a signed(More)
In this paper we consider the problem of solving geometric reconstruction problems with the L∞-norm. Previous work has shown that globally optimal solutions can be computed reliably for a series of such problems. The methods for computing the solutions have relied on the property of quasiconvexity. For quasiconvex problems, checking if there exists a(More)
This paper presents a new framework for solving geometric structure and motion problems based on the L1-norm. Instead of using the common sum-of-squares cost function, that is, the L2-norm, the model-fitting errors are measured using the L1-norm. Unlike traditional methods based on L2, our framework allows for the efficient computation of global estimates.(More)
Graph cuts methods are at the core of many state-of-the-art algorithms in computer vision due to their efficiency in computing globally optimal solutions. In this paper, we solve the maximum flow/minimum cut problem in parallel by splitting the graph into multiple parts and hence, further increase the computational efficacy of graph cuts. Optimality of the(More)
In this paper several new methods for estimating scene structure and camera motion from an image sequence taken by affine cameras are presented. All methods can incorporate both point, line and conic features in a unified manner. The correspondence between features in different images is assumed to be known. Three new tensor representations are introduced(More)