A comparison of methods for non-rigid 3D shape retrieval
نویسندگان
چکیده
Non-rigid 3D shape retrieval has become an active and important research topic in contentbased 3D object retrieval. The aim of this paper is to measure and compare the performance of state-of-the-art methods for non-rigid 3D shape retrieval. The paper develops a new benchmark consisting of 600 non-rigid 3D watertight meshes, which are equally classified into 30 categories, to carry out experiments for 11 different algorithms, whose retrieval accuracies are evaluated using 6 commonly-utilized measures. Models and evaluation tools of the new benchmark are publicly available on our web site [1].
منابع مشابه
SHREC ’ 15 Track : Non - rigid 3 D Shape Retrieval †
Non-rigid 3D shape retrieval has become a research hotpot in communities of computer graphics, computer vision, pattern recognition, etc. In this paper, we present the results of the SHREC’15 Track: Non-rigid 3D Shape Retrieval. The aim of this track is to provide a fair and effective platform to evaluate and compare the performance of current non-rigid 3D shape retrieval methods developed by d...
متن کاملSHREC '11 Track: Shape Retrieval on Non-rigid 3D Watertight Meshes
Non-rigid 3D shape retrieval has become an important research direction in content-based 3D object retrieval. The aim of this track is to measure and compare the performance of non-rigid 3D shape retrieval methods implemented by different participants around the world. The track is based on a new non-rigid 3D shape benchmark, which contains 600 watertight triangle meshes that are equally classi...
متن کاملSHREC'10 Track: Non-rigid 3D Shape Retrieval
Non-rigid shape matching is one of the most challenging fields in content-based 3D object retrieval. The aim of the SHREC 2010 Shape Retrieval Contest of Non-rigid 3D Models is to evaluate and compare the effectiveness of different methods run on a non-rigid 3D shape benchmark consisting of 200 watertight triangular meshes. Three groups with six methods have participated in this track and the r...
متن کامل3D Models Recognition in Fourier Domain Using Compression of the Spherical Mesh up to the Models Surface
Representing 3D models in diverse fields have automatically paved the way of storing, indexing, classifying, and retrieving 3D objects. Classification and retrieval of 3D models demand that the 3D models represent in a way to capture the local and global shape specifications of the object. This requires establishing a 3D descriptor or signature that summarizes the pivotal shape properties of th...
متن کاملSHREC’15 Track: Canonical Forms for Non-Rigid 3D Shape Retrieval
We present a new benchmark for testing algorithms that create canonical forms for use in non-rigid 3D shape retrieval. We have combined two existing datasets to create a varied collection of models for testing. Canonical forms attempt to factor out a shape’s pose, giving a pose-neutral shape. This opens up the possibility of using methods originally designed for rigid retrieval for the task of ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Pattern Recognition
دوره 46 شماره
صفحات -
تاریخ انتشار 2013