Exploiting Manifold Feature Representation for Efficient Classification of 3D Point Clouds

Abstract

In this paper, we propose an efficient point cloud classification method via manifold learning based feature representation. Different from conventional methods, we use manifold learning algorithms to embed point cloud features for better considering the geometric continuity on the surface. Then, the nature of point cloud can be acquired in low dimensional space, and after being concatenated with features in the original three-dimensional (3D) space, both the capability of feature representation and the classification network performance can be improved. We explore three traditional manifold algorithms ( i.e. , Isomap, Locally-Linear Embedding, and Laplacian eigenmaps) in detail, and finally, we select the Locally-Linear Embedding (LLE) algorithm due to its low complexity and locality consistency preservation. Furthermore, we propose a neural network based manifold learning (NNML) method to implement manifold learning based non-linear projection. Experiments demonstrate that the proposed two manifold learning methods can obtain better performances than the state-of-the-art methods, and the obtained mean class accuracy (mA) and overall accuracy (oA) can reach 91.4% and 94.4%, respectively. Moreover, because of the improved feature learning capability, the proposed NNML method can also have better classification accuracy on models with prominent geometric shapes. To further demonstrate the advantages of PointManifold, we extend it as a plug and play method for point cloud classification task, which can be directly used with existing methods and gain a significant improvement.

Improving ISOMAP Efficiency with RKS: A Comparative Study with t-Distributed Stochastic Neighbor Embedding on Protein Sequences

“…They demonstrated that their approach achieves comparable performance to the original ISOMAP algorithm while significantly reducing the computation time. Another approach proposed by [20] utilizes landmark-based approximation, where a small set of landmark points is selected to approximate the pairwise distances in the high-dimensional space. By constructing a low-dimensional embedding based on the landmark distances, the computational complexity of ISOMAP is effectively reduced.…”