Alexander Patterson

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We propose a novel technique for the registration of 3D point clouds which makes very few assumptions: we avoid any manual rough alignment or the use of landmarks, displacement can be arbitrarily large, and the two point sets can have very little overlap. Crude alignment is achieved by estimation of the 3D-rotation from two Extended Gaus-sian Images even(More)
— We propose a new method for extrinsic calibration of a line-scan LIDAR with a perspective projection camera. Our method is a closed-form, minimal solution to the problem. The solution is a symbolic template found via variable elimination and the multi-polynomial Macaulay resultant. It does not require initialization, and can be used in an automatic(More)
We propose an approach for detecting objects in large-scale range datasets that combines bottom-up and top-down processes. In the bottom-up stage, fast-to-compute local descriptors are used to detect potential target objects. The object hypotheses are verified after alignment in a top-down stage using global descriptors that capture larger scale structure(More)
We present a novel approach on digitizing large scale un-structured environments like archaeological excavations using off-the-shelf digital still cameras. The cameras are calibrated with respect to few markers captured by a theodo-lite system. Having all cameras registered in the same coordinate system enables a volumetric approach. Our new algorithm has(More)
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