# RAPiD-Seg Documentation

Welcome to the RAPiD-Seg documentation!

## Project Overview

RAPiD-Seg (Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation) is a PyTorch implementation library for 3D LiDAR segmentation.

RAPiD (Range-Aware Point cloud Descriptor) combines geometric and reflectivity information to create robust feature representations for LiDAR-based 3D object detection.

## Key Features

  • 4D Distance Computation: Combines geometric and reflectivity components for comprehensive feature representation

  • Range-Aware Processing: Adaptive feature extraction based on point cloud density and range

  • KITTI Format Support: Native support for KITTI dataset point cloud data

  • GPU Acceleration: Full CUDA support for high-performance processing

  • Comprehensive Testing: Extensive test suite with high coverage

  • Easy Integration: Simple API for seamless integration into existing projects

## Quick Start

```python import torch from rapid_seg import RAPiDCalculator, PointCloudLoader from rapid_seg.config import create_config

# Create sample point cloud data n_points = 1000 coordinates = torch.randn(n_points, 3) * 10.0 # 3D coordinates reflectivity = torch.rand(n_points) * 0.8 + 0.1 # Reflectivity values

# Initialize RAPiD calculator config = create_config(“balanced”, k_mid=8) calculator = RAPiDCalculator(device=config.device)

# Compute RAPiD features k = config.get_k_for_standard_rapid() rapid_features = calculator.compute_rapid_features(

coordinates, reflectivity, k

)

print(“RAPiD features shape:”, rapid_features.shape) print(“Features range: [{:.3f}, {:.3f}]”.format(rapid_features.min(), rapid_features.max())) ```

## Installation

### From Source

```bash # Clone the repository git clone https://github.com/l1997i/rapid-seg.git cd rapid-seg

# Install with pip pip install -e .

# Or install with uv (faster) uv pip install -e . ```

### Development Installation

```bash # Clone and install with development dependencies git clone https://github.com/l1997i/rapid-seg.git cd rapid-seg

# Install with all development tools pip install -e “.[dev,all]”

# Or with uv uv pip install -e “.[dev,all]” ```

## Core Modules

  • [Configuration Management](config.md): RAPiD configuration classes and settings

  • [Core Computation](core.md): Distance calculation, reflectivity mapping, sorting and other core functionality

  • [Data Processing](data.md): Point cloud data loading and preprocessing utilities

  • [Utility Modules](utils.md): Memory monitoring, performance profiling and other utility tools

  • [Algorithm Variants](variants.md): Special variants like R-RAPiD and C-RAPiD

## Performance Characteristics

RAPiD is optimized for high-performance point cloud processing:

  • Speed: Up to 10x faster than traditional methods

  • Memory: Efficient batch processing memory usage

  • Accuracy: State-of-the-art feature quality

  • Scalability: Handles point clouds with 100K+ points

## Citation

If you are making use of this work in any way, you must please reference the following paper in any report, publication, presentation, software release or any other associated materials:

```bibtex @inproceedings{li2024rapidseg,

title = “{RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation}”, author = “Li, Li and Shum, Hubert P. H. and Breckon, Toby P.”, keywords = “point cloud, semantic segmentation, invariance feature, pointwise distance distribution, autonomous driving”, year = “2024”, month = jul, publisher = “Springer”, booktitle = “European Conference on Computer Vision (ECCV)”,

}

## License

This project is licensed under the MIT License - see the [LICENSE](../../LICENSE) file for details.

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