WebJan 1, 2014 · We improve non-rigid surface correspondence by robust computing of the canonical pose. ... the SHREC Non-Rigid 3D Models dataset 2010 (SHREC-NR10) and the SHREC 2011 Non Rigid 3D ... R.P. Horaud, R. Kimmel, D. Knossow, E. von Lavante, D. Mateus, M. Ovsjanikov, A. Sharma, Shrec 2010: robust correspondence benchmark, in: …
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Web7 We evaluate the effectiveness of our approach using the SHREC’2010 robust correspondence benchmark. [sent-8, score-0.11] 8 Introduction The problem of finding correspondences in 3D shapes is an important problem in computer vision and computer graphics. [sent-11, score-0.449] WebSHREC 2010: robust correspondence benchmark. A Bronstein, MM Bronstein, U Castellani, A Dubrovina, LJ Guibas, ... Eurographics Workshop on 3D Object Retrieval 10, 087-091, 2010. 69: ... 2010: Machine learning in computer-aided diagnosis: Medical imaging intelligence and analysis: Medical imaging intelligence and analysis ... how is rabies transmitted from cat to human
Ivan Sipiran
WebThe matching process is guided by the similarity between regions inhigh levels of the tree, until reaching the keypoints stored in the leaves. This allows us to reduce the search space of correspondences, making also the matching process efficient. We evaluate the effectiveness of our approach using the SHREC’2010 robust correspondence benchmark. WebMay 1, 2024 · Extensive experiments have confirmed that our newly-learned shape descriptors have many attractive properties, including being concise, discriminative, and robust, and we demonstrate the superior performance of our approach over other state-of-the-art methods. References WebWe evaluate the effectiveness of our approach using the SHREC’2010 robust correspondence benchmark. In addition, we show that our results outperform the state of the art. Key-component Detection on 3D Meshes using Local Features We present a method to detect stable components on 3D meshes. how is race both a reality and a myth