Birdal T, Busam B, Navab N, Ilic S, Sturm P (2020)
Publication Type: Journal article
Publication year: 2020
Book Volume: 42
Pages Range: 1333-1347
Article Number: 8644023
Journal Issue: 6
DOI: 10.1109/TPAMI.2019.2900309
We present a novel and effective method for detecting 3D primitives in cluttered, unorganized point clouds, without axillary segmentation or type specification. We consider the quadric surfaces for encapsulating the basic building blocks of our environments-planes, spheres, ellipsoids, cones or cylinders, in a unified fashion. Moreover, quadrics allow us to model higher degree of freedom shapes, such as hyperboloids or paraboloids that could be used in non-rigid settings. We begin by contributing two novel quadric fits targeting 3D point sets that are endowed with tangent space information. Based upon the idea of aligning the quadric gradients with the surface normals, our first formulation is exact and requires as low as four oriented points. The second fit approximates the first, and reduces the computational effort. We theoretically analyze these fits with rigor, and give algebraic and geometric arguments. Next, by re-parameterizing the solution, we devise a new local Hough voting scheme on the null-space coefficients that is combined with RANSAC, reducing the complexity from O(N
APA:
Birdal, T., Busam, B., Navab, N., Ilic, S., & Sturm, P. (2020). Generic Primitive Detection in Point Clouds Using Novel Minimal Quadric Fits. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(6), 1333-1347. https://doi.org/10.1109/TPAMI.2019.2900309
MLA:
Birdal, Tolga, et al. "Generic Primitive Detection in Point Clouds Using Novel Minimal Quadric Fits." IEEE Transactions on Pattern Analysis and Machine Intelligence 42.6 (2020): 1333-1347.
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