2 research outputs found
Mesh Interest Point Detection Based on Geometric Measures and Sparse Refinement
Three dimensional (3D) interest point detection plays a fundamental role in
3D computer vision and graphics. In this paper, we introduce a new method for
detecting mesh interest points based on geometric measures and sparse
refinement (GMSR). The key point of our approach is to calculate the 3D
interest point response function using two intuitive and effective geometric
properties of the local surface on a 3D mesh model, namely Euclidean distances
between the neighborhood vertices to the tangent plane of a vertex and the
angles of normal vectors of them. The response function is defined in
multi-scale space and can be utilized to effectively distinguish 3D interest
points from edges and flat areas. Those points with local maximal 3D interest
point response value are selected as the candidates of 3D interest points.
Finally, we utilize an norm based optimization method to refine the
candidates of 3D interest points by constraining its quality and quantity.
Numerical experiments demonstrate that our proposed GMSR based 3D interest
point detector outperforms current several state-of-the-art methods for
different kinds of 3D mesh models.Comment: 17 page
3D Keypoint Detection Based on Deep Neural Network with Sparse Autoencoder
Researchers have proposed various methods to extract 3D keypoints from the
surface of 3D mesh models over the last decades, but most of them are based on
geometric methods, which lack enough flexibility to meet the requirements for
various applications. In this paper, we propose a new method on the basis of
deep learning by formulating the 3D keypoint detection as a regression problem
using deep neural network (DNN) with sparse autoencoder (SAE) as our regression
model. Both local information and global information of a 3D mesh model in
multi-scale space are fully utilized to detect whether a vertex is a keypoint
or not. SAE can effectively extract the internal structure of these two kinds
of information and formulate high-level features for them, which is beneficial
to the regression model. Three SAEs are used to formulate the hidden layers of
the DNN and then a logistic regression layer is trained to process the
high-level features produced in the third SAE. Numerical experiments show that
the proposed DNN based 3D keypoint detection algorithm outperforms current five
state-of-the-art methods for various 3D mesh models.Comment: 13 pages, 6 figure