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Monocular-Depth-Estimation-Toolbox

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Monocular Depth Estimation Toolbox based on MMSegmentation.

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# Monocular-Depth-Estimation-Toolbox
## Introduction

Monocular-Depth-Estimation-Toolbox is an open source monocular depth estimation toolbox based on PyTorch and [MMSegmentation](https://github.com/open-mmlab/mmsegmentation) v0.16.0.

It aims to benchmark MonoDepth methods and provides effective supports for evaluating and visualizing results.

### Major features
- **Unified benchmark**
  
  Provide a unified benchmark toolbox for various depth estimation methods.
- **Modular design**
  
  Depth estimation frameworks are decomposed into different components. One can easily construct a customized framework by combining different modules.
- **Support of multiple methods out of box**
  
  I would like to reproduce some of the most excellent depth estimation methods based on this toolbox.
- **High efficiency**
  
  It seems that there are few depth estimation benchmarks, so I start this project and hope it is helpful for research.

Thanks to MMSeg, we own these major features. :blush:

## Benchmark and model zoo

Results and models are available in the [model zoo](docs/model_zoo.md).

Supported backbones (partially release):
- [x] ResNet (CVPR'2016)
- [x] EfficientNet (ICML'2019)
- [x] Vision Transformer (ICLR'2021)
- [x] Swin Transformer (ICCV'2021)
- I recommend cross-package import in config, so that you can utilize other backbone in MMcls, MMseg, *etc*. Refer to [introduction](https://zhuanlan.zhihu.com/p/436865195). I will add more backbones in the future.

Supported methods:
- [x] [BTS (Arxiv'2019)](configs/bts)
- [x] [Adabins (CVPR'2021)](configs/adabins)
- [x] [SimIPU (AAAI'2022)](configs/simipu)
- [x] [DPT (ICCV'2021) (TBD)](configs/dpt)
- [x] [NeWCRFs (CVPR'2022) (TBD)](configs/newcrfs)
- [x] [DepthFormer (Arxiv'2022)](configs/depthformer)
- [x] [BinsFormer (Arxiv'2022)](configs/binsformer) (Early release. It may contain bugs or potential performance drop)

Supported datasets:
- [x] [KITTI](docs/dataset_prepare.md#KITTI)
- [x] [NYU-Depth-V2](docs/dataset_prepare.md#NYU)
- [x] [SUN RGB-D (Eval)](docs/dataset_prepare.md#SUNRGBD)
- [x] [Cityscapes](docs/dataset_prepare.md#Cityscapes)

## Installation

Please refer to [get_started.md](docs/get_started.md#installation) for installation and [dataset_prepare.md](docs/dataset_prepare.md#prepare-datasets) for dataset preparation.

## Get Started

We provide [train.md](docs/train.md) and [inference.md](docs/inference.md) for the usage of this toolbox.

## License

This project is released under the [Apache 2.0 license](LICENSE).

## Acknowledgement

This repo benefits from awesome works of [mmsegmentation](https://github.com/open-mmlab/mmsegmentation), [Adabins](https://github.com/shariqfarooq123/AdaBins),
[BTS](https://github.com/cleinc/bts). Please also consider citing them.

## Cite
```bibtex
@misc{lidepthtoolbox2022,
  title={Monocular Depth Estimation Toolbox},
  author={Zhenyu Li},
  howpublished = {\url{https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox}},
  year={2022}
}
```

## Changelog
- **Nov. 29, 2022**: Release the config for BinsFormer on KITTI. Release configs for cross-dataset inference (nyu2sunrgbd). Fix NYUv2 download bugs as discussed in Issue [#66](https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox/issues/66) and [#36](https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox/issues/36). I add several codes for NeWCRFs but have no plan to conduct detailed experiments for it due to the current limited resource and time.
- **Jul. 23, 2022**: Release codes for BinsFormer. Potential bugs or performance drops can exist, waiting for completion.
- **Jun. 5, 2022**: Add support for custom dataset training. Add a warmup interface for sigloss to help convergence as discussed in Issue [#20](https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox/issues/20). Enhance the DPT support and fix bugs in provided pre-trained models as reported in Issue [#23](https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox/issues/23). 
- **Apr. 16, 2022**: Finish most of docs and provide all pre-trained parameters. Release codes about BTS, Adabins, DPT, SimIPU, and DepthFormer. Support KITTI, NYU-v2, SUN RGB-D(eval), and CityScapes.

## TODO
- I will complete the release of BinsFormer models on KITTI dataset (currently limited by the GPU resource). 
- I would like to include self-supervised depth estimation methods, such as MonoDepth2.

Monocular-Depth-Estimation-Toolbox

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