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Official Repo for Ground-aware Monocular 3D Object Detection for Autonomous Driving / YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection
| Date | Stars |
|---|---|
| 2026-07-24 | 398 |
| 2026-07-25 | 398 |
| 2026-07-28 | 398 |
| 2026-07-30 | 398 |
| 2026-08-06 | 398 |
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# Visual 3D Detection Package:
This repo aims to provide flexible and reproducible visual 3D detection on KITTI dataset. We expect scripts starting from the current directory, and treat ./visualDet3D as a package that we could modify and test directly instead of a library. Several useful scripts are provided in the main directory for easy usage.
We believe that visual tasks are interconnected, so we make this library extensible to more experiments.
The package uses registry to register datasets, models, processing functions and more, allowing easy inserting of new tasks/models while not interfere with the existing ones.
## Related Paper:
This repo contains the official implementation of 2021 *RAL* \& *ICRA* paper [**Ground-aware Monocular 3D Object Detection for Autonomous Driving**](https://ieeexplore.ieee.org/document/9327478). [Arxiv Page](https://arxiv.org/abs/2102.00690). Pretrained model can be found at [release pages](https://github.com/Owen-Liuyuxuan/visualDet3D/releases/tag/1.0).
```
@ARTICLE{9327478,
author={Y. {Liu} and Y. {Yuan} and M. {Liu}},
journal={IEEE Robotics and Automation Letters},
title={Ground-aware Monocular 3D Object Detection for Autonomous Driving},
year={2021},
doi={10.1109/LRA.2021.3052442}}
```
Also the official implementation of 2021 *ICRA* paper [**YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection**](https://arxiv.org/abs/2103.09422). Pretrained model can be found at [release pages](https://github.com/Owen-Liuyuxuan/visualDet3D/releases/tag/1.1).
```
@inproceedings{liu2021yolostereo3d,
title={YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection},
author={Yuxuan Liu and Lujia Wang and Ming, Liu},
booktitle={2021 International Conference on Robotics and Automation (ICRA)},
year={2021},
organization={IEEE}
}
```
We further incorperate an *Unofficial* re-implementation of **Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training** (KM3D) as a reference on how to integrate with other frameworks. (Notice that the codes are from the [originally official repo](https://github.com/Banconxuan/RTM3D), and we **DO NOT** guarantee a complete re-implementation).
Update (2021.07.02): We provide an *Unofficial* re-implementation of **Objects are Different: Flexible Monocular 3D Object Detection** (MonoFlex) with few additional codes, based on the KM3D structure. Many of the core codes are from [original official repo](https://github.com/zhangyp15/MonoFlex). We did not implement the edge merge operation and the corner loss, but we manage to maintain most of the performance based on the proposed depth fusion methods(validation AP reaches 15%).
Update (2021.12.11): We provide an *Unofficial* re-implmentation of **Digging Into Output Representation For Monocular 3D Object Detection** (Digging_M3D) to introduce an simple but important numerical trick to significantly improve the KITTI mAP scores and make a significant change to the KITTI leaderboard. Details can be found in the [paper](https://openreview.net/forum?id=mPlm356yMIP). At the time of the open-source, the paper has not been officially published, and we will keep up with the update of the paper.
## Key Features
- **SOTA Performance** State of the art result on visual 3D detection.
- **Modular Design** Modular design for dataset, network and running pipelines.
- **Support Various Task** Compatible with the training and testing of mono/stereo 3D detection and depth prediction.
- **Distributed & Single GPU** Support training with multiple GPUs.
- **Installation-Free Setup** The setup process only build operations and does not require installation to keep the environment clean.
- **Global Path-based IMDB** Do not need data placed inside the folder, convienient for managing data and code separately.
We provide start-up solutions for [Mono3D](docs/mono3d.md), [Stereo3D](docs/stereo3d.md), [Depth Predictions](docs/monoDepth.md) and more (until further publication). We Excerpt of 7,243 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f14a88d2f3ea9432, topic:computer-vision, topic:object-detection, desc:object detection