PyTorch library for BEV style perception models
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 `
` $ git clone --recursive https://github.com/d4l3k/torchdrive.git $ cd torchdrive $ pip install -e . `
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.
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:
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
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