2,609 research outputs found
Salient Local 3D Features for 3D Shape Retrieval
In this paper we describe a new formulation for the 3D salient local features
based on the voxel grid inspired by the Scale Invariant Feature Transform
(SIFT). We use it to identify the salient keypoints (invariant points) on a 3D
voxelized model and calculate invariant 3D local feature descriptors at these
keypoints. We then use the bag of words approach on the 3D local features to
represent the 3D models for shape retrieval. The advantages of the method are
that it can be applied to rigid as well as to articulated and deformable 3D
models. Finally, this approach is applied for 3D Shape Retrieval on the McGill
articulated shape benchmark and then the retrieval results are presented and
compared to other methods.Comment: Three-Dimensional Imaging, Interaction, and Measurement. Edited by
Beraldin, J. Angelo; Cheok, Geraldine S.; McCarthy, Michael B.;
Neuschaefer-Rube, Ulrich; Baskurt, Atilla M.; McDowall, Ian E.; Dolinsky,
Margaret. Proceedings of the SPIE, Volume 7864, pp. 78640S-78640S-8 (2011).
Conference Location: San Francisco Airport, California, USA ISBN:
9780819484017 Date: 10 March 201
Skeleton-based canonical forms for non-rigid 3D shape retrieval
The retrieval of non-rigid 3D shapes is an important task. A common technique is to simplify this problem to a rigid shape retrieval task by producing a bending invariant canonical form for each shape in the dataset to be searched. It is common for these techniques to attempt to ``unbend'' a shape by applying multidimensional scaling to the distances between points on the mesh, but this leads to unwanted local shape distortions. We instead perform the unbending on the skeleton of the mesh, and use this to drive the deformation of the mesh itself. This leads to a computational speed-up and less distortions of the local details of the shape. We compare our method against other canonical forms and our experiments show that our method achieves state-of-the-art retrieval accuracy in a recent canonical forms benchmark, and only a small drop in retrieval accuracy over state-of-the-art in a second recent benchmark, while being significantly faster
- …