Point Cloud Processing, Classification, and Segmentation

This repository contains code and resources for processing point cloud data, performing classification, and segmentation tasks using the Open3D library, the ModelNet10 dataset, and the PointNet network.

Introduction

Point clouds are a fundamental data format used in various computer vision and 3D perception tasks. This repository is aimed at helping you process, classify, and segment point cloud data using Open3D, ModelNet10 dataset, and the PointNet network.

Dependencies

Before using the code in this repository, make sure you have the following dependencies installed:

Usage

Download the ModelNet10 dataset and preprocess it:

To get started, follow these steps to download the ModelNet10 dataset and prepare it for your project:

Download the ModelNet10 dataset. You may need to register or accept their terms and conditions.
Once downloaded, extract the dataset files to a directory of your choice.
Preprocess the dataset if necessary. This may include data normalization, format conversion, or other steps specific to your project. Document these preprocessing steps to make the dataset ready for your code.

Run the point cloud processing code:

To process point clouds, follow these instructions:

Clone this repository to your local machine:

git clone https://github.com/yourusername/point-cloud-processing.git
cd point-cloud-processing

Install any required dependencies using pip:
```
    pip install -r requirements.txt
```
Run the point cloud processing code, ensuring you provide any necessary configuration parameters or input data.
Document the process and provide example code for running the point cloud processing code.

Training and Evaluating the PointNet Classifier

PointNet Classifier

Architecture

In this project, we employ a PointNet classifier to process and classify 3D point cloud data. Our implementation is based on the original PointNet architecture with some modifications tailored for the ModelNet10 dataset.

Training

To train the PointNet classifier, follow these steps:

Data Splitting: Start by dividing the ModelNet10 dataset into training, validation, and testing sets. You can do this using the provided data splitting scripts.
Training Parameters: Set the training parameters such as batch size, learning rate, and the number of epochs. You can adjust these parameters in the configuration files.
Optimization Techniques: We use standard optimization techniques such as stochastic gradient descent (SGD) or Adam. These settings are also configurable in the training scripts.
Training Process: Execute the training script with the chosen settings. The model will be trained on the training dataset, and you can monitor the training progress through the provided logs.

Evaluation

After training, you can evaluate the classifier's performance using the validation and test datasets. We provide evaluation scripts for this purpose. The following evaluation metrics are included:

Accuracy
Confusion Matrix
Precision
Recall
F1-Score

Segmentation

Approach to Point Cloud Segmentation

In this section, we describe our approach to point cloud segmentation. We employ state-of-the-art segmentation techniques and algorithms to extract meaningful regions from point cloud data. Detailed instructions and example code for performing segmentation tasks are available in our segmentation module.