| Name | kaist-dataloader JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | Efficient and user-friendly point cloud data loader for the KAIST dataset, supporting multiple coordinate systems and numpy compatibility. |
| upload_time | 2025-08-23 03:49:49 |
| maintainer | None |
| docs_url | None |
| author | HopeCollector |
| requires_python | >=3.12 |
| license | MIT License Copyright (c) 2025 HopeCollector Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
kaist
pointcloud
lidar
slam
autonomous driving
dataset
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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| coveralls test coverage |
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|
[中文文档](README_CN.md)
> This document is a translation of the original Chinese documentation. For the original version, please refer to `README_CN.md`.
# KAIST Dataloader
> Easily read point cloud data and pose information from the KAIST dataset.
> **Dataset Homepage**: [KAIST Complex Urban Dataset](https://sites.google.com/view/complex-urban-dataset/home)
## Project Overview
This project aims to provide an efficient and user-friendly point cloud data loader for the KAIST dataset, supporting multiple coordinate system conversions, iteration, slicing operations, and more. It is suitable for scenarios such as autonomous driving, SLAM, and 3D reconstruction.
## Features
- **Multiple Coordinate Systems Supported**:
- `base_link`: Vehicle local coordinate system
- `map`: Coordinate system with the first frame as the origin
- `world`: Global GPS coordinate system
- **Iterator & Slicing**: Supports iteration, slicing, and random access
- **Numpy Compatibility**: Can read directly from numpy arrays
- **Dataset Length Retrieval**: Supports the `len()` method
- **Mixed Loading**: Automatically mixes and loads VLP_Left and VLP_Right point clouds sorted by timestamp
## Installation
```bash
pip install kaist-dataloader
```
Or install from source:
```bash
pip install -e .
```
## Quick Start
```python
from kaist_dataloader import KaistPointCloudLoader
loader = KaistPointCloudLoader("/path/to/kaist/campus00", frame_id="map")
cld, pose = loader[0] # Read the first point cloud and its pose
# Iterative access
for cld, pose in loader[:10]:
# Process point cloud and pose
pass
# Get dataset length
print(len(loader))
```
## C++/pybind11 Example
This project supports direct invocation of the Python loader in C++ via pybind11, suitable for integration with C++ projects.
Sample code can be found in `example/cpp/main.cc`. Ensure the `root` path points to a valid KAIST dataset directory.
### Build and Run Steps
1. **Activate Python virtual environment** (ensure kaist-dataloader and pybind11 are installed)
2. Modify line 18 in `main.cc` to set the `root` variable to your dataset path, e.g., `/ws/data/kaist/campus00`
3. Build the project:
```bash
cd example/cpp
cmake -S . -B build
cmake --build build
```
4. Run the example:
```bash
./build/embed_loader
```
After running, the output will show the dataset length, and the shapes of the point cloud and pose.
## Notes
- The loader mixes left and right LiDAR data by default, and the returned pose is in the specified coordinate system.
- For fine-grained control over loading, use the `kaist_dataloader.basic` module.
- Point cloud data format: `[x, y, z, intensity]`; pose format: `4x4` transformation matrix.
## Dependencies
- numpy
- scipy
Development/testing dependencies:
- open3d
- matplotlib
- pytest
- ipykernel
## Changelog
- **v0.1.0**: Initial version, implemented the
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"description": "[\u4e2d\u6587\u6587\u6863](README_CN.md)\n\n> This document is a translation of the original Chinese documentation. For the original version, please refer to `README_CN.md`.\n\n# KAIST Dataloader\n\n> Easily read point cloud data and pose information from the KAIST dataset.\n> **Dataset Homepage**: [KAIST Complex Urban Dataset](https://sites.google.com/view/complex-urban-dataset/home)\n\n## Project Overview\n\nThis project aims to provide an efficient and user-friendly point cloud data loader for the KAIST dataset, supporting multiple coordinate system conversions, iteration, slicing operations, and more. It is suitable for scenarios such as autonomous driving, SLAM, and 3D reconstruction.\n\n## Features\n\n- **Multiple Coordinate Systems Supported**: \n - `base_link`: Vehicle local coordinate system \n - `map`: Coordinate system with the first frame as the origin \n - `world`: Global GPS coordinate system\n- **Iterator & Slicing**: Supports iteration, slicing, and random access\n- **Numpy Compatibility**: Can read directly from numpy arrays\n- **Dataset Length Retrieval**: Supports the `len()` method\n- **Mixed Loading**: Automatically mixes and loads VLP_Left and VLP_Right point clouds sorted by timestamp\n\n## Installation\n\n```bash\npip install kaist-dataloader\n```\n\nOr install from source:\n\n```bash\npip install -e .\n```\n\n## Quick Start\n\n```python\nfrom kaist_dataloader import KaistPointCloudLoader\n\nloader = KaistPointCloudLoader(\"/path/to/kaist/campus00\", frame_id=\"map\")\ncld, pose = loader[0] # Read the first point cloud and its pose\n\n# Iterative access\nfor cld, pose in loader[:10]:\n # Process point cloud and pose\n pass\n\n# Get dataset length\nprint(len(loader))\n```\n\n## C++/pybind11 Example\n\nThis project supports direct invocation of the Python loader in C++ via pybind11, suitable for integration with C++ projects. \nSample code can be found in `example/cpp/main.cc`. Ensure the `root` path points to a valid KAIST dataset directory.\n\n### Build and Run Steps\n\n1. **Activate Python virtual environment** (ensure kaist-dataloader and pybind11 are installed) \n2. Modify line 18 in `main.cc` to set the `root` variable to your dataset path, e.g., `/ws/data/kaist/campus00`\n3. Build the project:\n ```bash\n cd example/cpp\n cmake -S . -B build\n cmake --build build\n ```\n4. Run the example:\n ```bash\n ./build/embed_loader\n ```\n\nAfter running, the output will show the dataset length, and the shapes of the point cloud and pose.\n\n## Notes\n\n- The loader mixes left and right LiDAR data by default, and the returned pose is in the specified coordinate system.\n- For fine-grained control over loading, use the `kaist_dataloader.basic` module.\n- Point cloud data format: `[x, y, z, intensity]`; pose format: `4x4` transformation matrix.\n\n## Dependencies\n\n- numpy\n- scipy\n\nDevelopment/testing dependencies:\n- open3d\n- matplotlib\n- pytest\n- ipykernel\n\n## Changelog\n\n- **v0.1.0**: Initial version, implemented the",
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