# CaDDN

**Repository Path**: armyzzZ/CaDDN

## Basic Information

- **Project Name**: CaDDN
- **Description**: No description available
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-09-27
- **Last Updated**: 2021-09-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<img src="docs/trailab.png" align="right" width="20%">

# CaDDN

`CaDDN` is a monocular-based 3D object detection method. This repository is based off of [`[OpenPCDet]`](https://github.com/open-mmlab/OpenPCDet).

**Categorical Depth Distribution Network for Monocular 3D Object Detection**\
Cody Reading, Ali Harakeh, Julia Chae, and Steven L. Waslander\
**[[Paper](https://arxiv.org/abs/2103.01100)]**


## Overview
- [Changelog](#changelog)
- [Model Zoo](#model-zoo)
- [Installation](docs/INSTALL.md)
- [Getting Started](docs/GETTING_STARTED.md)
- [Citation](#citation)


## Changelog
[2021-03-16] `CaDDN` v0.3.0 is released.

## Introduction


### What does `CaDDN` do?

`CaDDN` is a general PyTorch-based method for 3D object detection from monocular images.
At the time of submission, `CaDDN` achieved first 1st place among published monocular methods on the [Kitti 3D object detection benchmark](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d). We welcome contributions to this project.

### `CaDDN` design pattern
We inherit the design pattern from [`[OpenPCDet]`](https://github.com/open-mmlab/OpenPCDet).

* Data-Model separation with unified point cloud coordinate for easily extending to custom datasets:
<p align="center">
  <img src="docs/dataset_vs_model.png" width="95%" height="320">
</p>

* Unified 3D box definition: (x, y, z, dx, dy, dz, heading).

## Model Zoo

### KITTI 3D Object Detection Baselines
Selected supported methods are shown in the below table. The results are the 3D detection performance of Car class on the *val* set of KITTI dataset.
* All models are trained with 2 Tesla T4 GPUs and are available for download.
* The training time is measured with 2 Tesla T4 GPUs and PyTorch 1.4.

|                                             | training time | Easy@R40 | Moderate@R40 | Hard@R40  | download |
|---------------------------------------------|----------:|:-------:|:-------:|:-------:|:---------:|
| [CaDDN](tools/cfgs/kitti_models/CaDDN.yaml) |~76 hours| 23.77 | 16.07 | 13.61 | [model-774M](https://drive.google.com/file/d/13HGW3_zCTKHGVtr_JDHD4Wv64PP5Z2mG/view?usp=sharing) |

## Installation

Please refer to [INSTALL.md](docs/INSTALL.md) for the installation of `CaDDN`.

## Getting Started

Please refer to [GETTING_STARTED.md](docs/GETTING_STARTED.md) to learn more usage about this project.


## License

`CaDDN` is released under the [Apache 2.0 license](LICENSE).

## Acknowledgement
`CaDDN` is an open source project for monocular-based 3D scene perception.
We would like to thank the authors of [`OpenPCDet`](https://github.com/open-mmlab/OpenPCDet) for their open-source release of their 3D object detection codebase.


## Citation
If you find this project useful in your research, please consider citing:
```
@article{CaDDN,
    title={Categorical Depth DistributionNetwork for Monocular 3D Object Detection},
    author={Cody Reading and
            Ali Harakeh and
            Julia Chae and
            Steven L. Waslander},
    journal = {CVPR},
    year={2021}
}
```


## Contribution
Welcome to be a member of the CaDDN development team by contributing to this repo, and feel free to contact us for any potential contributions.