# 
A PyTorch library for training BEV style perception models for self driving
tasks. This is unaffiliated with the PyTorch project. This currently includes
helpful primitives needed to put together a model.
## Install from Source
```
$ pip install git+https://github.com/d4l3k/torchdrive.git
```
or
```
$ git clone --recursive https://github.com/d4l3k/torchdrive.git
$ cd torchdrive
$ pip install -e .
```
## Background
I've been documenting the process for this code. Please see my blog at
https://fn.lc/post/3d-detr/ for more details.
### 3D Object Detection
3D bounding boxes and velocities for dynamic objects such as cars.
### Voxel Occupancy
Grids of occupancy around the vehicle trained with differential rendering.
### BEV Lane Lines and Drivable Space
Lane line and drivable space trained purely from image space labels.
### Semantic Voxel
Per voxel semantic labels for static objects.
## Data Access
The training dataset for this repo has been collected from my car and thus has
lots of personally identifying information so I'm not willing to make it public
at this time. If you're interested in contributing or collaborating feel free to
reach out. I'm happy to test changes on my own hardware and there may be other
options too.
## Contact
If you have any questions or concerns, please reach out to me either by filing
an issue or emailing me at [rice@fn.lc](mailto:rice@fn.lc).
## License
This project is a hobby project and done in my free time. This is non-commercial
and no profit has been made from this work.
See the [LICENSE](LICENSE) file for more information. Some files and functions
have different licenses and are marked accordingly.
BSD 3-Clause License
Copyright (c) 2023, Tristan Rice
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Raw data
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