With the rise of visualization, animation and autonomous driving applications, the demand for 3D point cloud analysis and understanding has rapidly increased. Point Cloud is a kind of data obtained from lidar scanning which contains abundant 3D information. ModelNet40 is a point cloud dataset contains 40 classes of objects. In this project, we use ModelNet40 dataset for the analysis and evaluation of point cloud classification. Many of the recent works focus on developing end to end algorithm like other convolutional neural networks for images. However, object and scene understanding with Convolutional Neural Networks (CNNs) on 3D volumetric data is still limited due to its high memory requirement and computational cost. For some simple tasks like classification, this method is too much.

An interpretable CNN design based on the feedforward (FF) methodology [1] without any backpropagation (BP) was recently proposed by the Media Communications Lab at USC. The classification baseline is composed by four Saab units, each unit contains KNN query, space grouping and Saab transform, and between units we use farthest sampling to improve efficiency. We are still working on it to improve as much as possible. Our goal is to catch up with the state-of-the-art results and show that FF design is powerful and useful. The advantages of the FF design methodology are multiple folds. It is completely interpretable. It demands much less training complexity and training data. Furthermore, it can be generalized to weakly supervised or unsupervised learning scenarios in a straightforward manner. The latter is extremely important in real world application scenarios since data labeling is very tedious and expensive.

The advantages of the FF design methodology are multiple folds. It is completely interpretable. It demands much less training complexity and training data. Furthermore, it can be generalized to weakly supervised or unsupervised learning scenarios in a straightforward manner. The latter is extremely important in real world application scenarios since data labeling is very tedious and expensive.

–Author: Min Zhang

Reference

[1] C.-C. J. Kuo, M. Zhang, S. Li, J. Duan and Y. Chen, “Interpretable Convolutional Neural Networks via Feedforward Design.” arXiv preprint arXiv:1810.02786 (2018).