Point Cloud Semantic Segmentation (PandaSet + PointNet)

This repository provides a minimal, clean, and extendable ML pipeline for 3D point cloud semantic segmentation using the PandaSet LiDAR dataset. It implements a complete workflow from dataset acquisition → preprocessing → model architecture → training stub.

The goal is to provide a working baseline that can later be upgraded to advanced 3D sparse convolution architectures (e.g., MinkowskiNet / SparseConv U-Net).

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🚀 Features

• Full dataset download via Python (Kaggle API)
• Custom PandaSet loader (point clouds + semantic labels)
• PointNet segmentation baseline implemented in PyTorch
• Minimal training loop (forward → loss → backward)
• Configurable number of sequences (use subsets for fast prototyping)
• Fully reproducible and dependency-light

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📦 Setup

pip install -r requirements.txt

Create a .env file in the project root:

KAGGLE_USERNAME=your_username
KAGGLE_KEY=your_key

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🗂 Dataset Download (PandaSet)

Run:

python download_pandaset.py

This script:

• Authenticates using your .env
• Downloads the full PandaSet dataset (~33GB) from Kaggle
• Unzips everything under ./pandaset/

PandaSet is released under CC-BY-NC-SA, and Kaggle enforces attribution automatically via the LICENSE file.

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Model Architecture — PointNet Baseline

The baseline model is a PointNet segmentation network implemented from scratch:

• Input: (N, 4) points → (x, y, z, intensity)
• MLP + shared Conv1D layers
• Global feature aggregation
• Per-point classification head

This provides a simple and reliable foundation before migrating to:

• SparseConv U-Net
• MinkowskiNet
• KPConv
• PointNet++
• etc.

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🏋️ Training

python pointnet_model.py

This runs:

• Dataset loading
• Minimal data analysis
• One epoch of training (stub)
• Prints batch loss

Designed for rapid validation and code completeness rather than full training.

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📊 Expected Baseline Performance

• PointNet on outdoor LiDAR: ~35–50% mIoU
• SparseConv U-Net (production target): ~60–75% mIoU

PointNet is intentionally chosen for simplicity and compatibility with the assessment constraints.

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🧱 Project Structure

point_cloud_segmentation/
│
├── download_pandaset.py        # Kaggle dataset downloader
├── pointnet_model.py           # Dataset loader + PointNet + training stub
├── MODEL_CHOICES.md            # Architecture justification & notes
├── requirements.txt            # Dependencies
└── .gitignore

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📘 License Notice

PandaSet is provided under the CC-BY-NC-SA 4.0 license. Any commercial use must follow Scale.ai's licensing terms.

This repository contains no dataset files — users must download them via Kaggle using the provided script.

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