{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/generic-primitive-detection-in-point-clouds","title":"Generic Primitive Detection in Point Clouds Using Novel Minimal Quadric Fits","arxiv_id":"1901.01255","date":"2019-01-04","proceeding":null,"authors":["Tolga Birdal","Benjamin Busam","Nassir Navab","Slobodan Ilic","Peter Sturm"],"abstract":"We present a novel and effective method for detecting 3D primitives in\ncluttered, unorganized point clouds, without axillary segmentation or type\nspecification. We consider the quadric surfaces for encapsulating the basic\nbuilding blocks of our environments - planes, spheres, ellipsoids, cones or\ncylinders, in a unified fashion. Moreover, quadrics allow us to model higher\ndegree of freedom shapes, such as hyperboloids or paraboloids that could be\nused in non-rigid settings.\n  We begin by contributing two novel quadric fits targeting 3D point sets that\nare endowed with tangent space information. Based upon the idea of aligning the\nquadric gradients with the surface normals, our first formulation is exact and\nrequires as low as four oriented points. The second fit approximates the first,\nand reduces the computational effort. We theoretically analyze these fits with\nrigor, and give algebraic and geometric arguments. Next, by re-parameterizing\nthe solution, we devise a new local Hough voting scheme on the null-space\ncoefficients that is combined with RANSAC, reducing the complexity from\n$O(N^4)$ to $O(N^3)$ (three points). To the best of our knowledge, this is the\nfirst method capable of performing a generic cross-type multi-object primitive\ndetection in difficult scenes without segmentation. Our extensive qualitative\nand quantitative results show that our method is efficient and flexible, as\nwell as being accurate.","url_abs":"http://arxiv.org/abs/1901.01255v1","url_pdf":"http://arxiv.org/pdf/1901.01255v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"generic-primitive-detection-in-point-clouds","repo_url":"https://github.com/tolgabirdal/phdimgeneralization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.01255","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}