https://github.com/TRAILab/CaDDN

Categorical Depth Distribution Network for Monocular 3D Object Detection (CVPR 2021 Oral)
https://github.com/TRAILab/CaDDN

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Categorical Depth Distribution Network for Monocular 3D Object Detection (CVPR 2021 Oral)

• Host: GitHub
• URL: https://github.com/TRAILab/CaDDN
• Owner: TRAILab
• License: apache-2.0
• Created: 2021-01-14T14:16:22.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2022-12-24T14:23:24.000Z (almost 2 years ago)
• Last Synced: 2024-08-01T03:44:08.495Z (4 months ago)
• Language: Python
• Homepage:
• Size: 523 MB
• Stars: 361
• Watchers: 4
• Forks: 61
• Open Issues: 23
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        


# 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:



  



* 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.