WebCiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): SHREC’10 robust correspondence benchmark simulates a one-to-one shape matching scenario, in which one of the shapes undergoes multiple modifications and transformations. The benchmark allows evaluating how correspondence algorithms cope with certain classes … WebIn SHREC 2010 contest [2], the first two ranked methods were view-based methods using manifold ranking as a post-processing step to improve the retrieval accuracy. In this paper, a complete ...
CiteSeerX — SHREC 2010: robust correspondence benchmark
WebSHREC’10 robust correspondence benchmark simulates a one-to-one shape matching scenario, in which one of the shapes undergoes multiple modifications and … WebThis track of the SHREC’16 contest evaluates shape matching algorithms that operate on 3D shapes under synthetically produced topological changes and describes the different methods and the contest results. A particularly challenging setting of the shape matching problem arises when the shapes being matched have topological artifacts due to the … chris long family residences
SHREC 2011 Proceedings of the 4th Eurographics …
WebThis paper is a report on the 3D Shape Retrieval Constest 2010 (SHREC’10) track on large scale retrieval. This benchmark allows evaluating how wel retrieval algorithms scale up to … WebHere, we extend the idea of heat kernel signature to robust isometry-invariant volumetric descriptors, and show their utility in shape retrieval. The proposed approach achieves state-of-the-art results on the SHREC 2010 large-scale shape retrieval benchmark. WebApr 23, 2024 · The author has evaluated the performance of the approach on non-rigid 3D dataset such as SHREC’2010 and SHREC’2011 by using split ratio of 30% for the test and 70% for the training. The average classification accuracy of 96.67% and 97.59% are respectively obtained on SHREC’2010 and SHREC’2011. Masoumi and Ben Hamza. [ 33] geoff neal twitter