Georgia Institute of Technology, Atlanta, GA
Final project by,
Devashish Gupta: devashish-gupta@gatech.edu
Divyaansh Singh: dsingh313@gatech.edu
Sanjay Josh: sjosh3@gatech.edu
Vastav Bharambe: vbharambe6@gatech.edu
Refer to driver.ipynb for all the main sections
Point cloud data is an important type of data used in various applications such as autonomous vehicles, robotics, and virtual reality. Point cloud classification refers to the task of assigning a label to a given point cloud based on its features.
In this notebook, we will be using the ModelNet40 dataset for testing our models. ModelNet10 is a widely-used benchmark dataset for evaluating 3D object classification algorithms. It contains 12,311 CAD models from 10 different categories, with each model represented as a point cloud.
We will be implementing and comparing three different models for point cloud classification: SimpleView, 3DCTN, and a modified custom version of 3DCTN. SimpleView is a simple yet effective model that uses a fully connected network to classify point clouds. 3DCTN, on the other hand, is a more complex model that uses a 3D convolutional neural network (CNN) to classify point clouds. We will also be modifying the 3DCTN model to see if we can improve its performance.
Additionally, we will also be testing each model against adversarial attacks, including one-point attacks and imperceptible noise addition attacks, to evaluate their robustness.