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https://github.com/ShuweiShao/IEBins

[NeurIPS2023] IEBins: Iterative Elastic Bins for Monocular Depth Estimation
https://github.com/ShuweiShao/IEBins

computer-vision deep-learning depth-estimation

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[NeurIPS2023] IEBins: Iterative Elastic Bins for Monocular Depth Estimation

Host: GitHub
URL: https://github.com/ShuweiShao/IEBins
Owner: ShuweiShao
License: mit
Created: 2023-09-22T04:33:42.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2024-06-05T01:47:15.000Z (5 months ago)
Last Synced: 2024-06-05T15:17:11.305Z (5 months ago)
Topics: computer-vision, deep-learning, depth-estimation
Language: Python
Homepage:
Size: 1.37 MB
Stars: 79
Watchers: 3
Forks: 4
Open Issues: 3
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

Awesome-Monocular-Depth - IEBins: Iterative Elastic Bins for Monocular Depth Estimation

README

        


IEBins: Iterative Elastic Bins for Monocular Depth Estimation




    Shuwei Shao¹ 

    Zhongcai Pei¹ 

    Xingming Wu¹ 

    Zhong Liu¹ 

    Weihai Chen² 

    Zhengguo Li³





    ¹Beihang University, ²Anhui University, ³A*STAR





    

        • NeurIPS 2023 •

    



[![KITTI Benchmark](https://img.shields.io/badge/KITTI%20Benchmark-2nd%20among%20all%20at%20submission%20time-blue)](https://www.cvlibs.net/datasets/kitti/eval_depth.php?benchmark=depth_prediction)

[![Hugging Space Badge](https://img.shields.io/badge/🤗-Open%20In%20Spaces-blue.svg)](https://huggingface.co/spaces/umuthopeyildirim/IEBins-Depth-Estimation)

## Abstract









We propose a novel concept of iterative elastic bins for the classification-regression-based MDE. The proposed IEBins aims to search for high-quality depth by progressively optimizing the search range, which involves multiple stages and each stage performs a finer-grained depth search in the target bin on top of its previous stage. To alleviate the possible error accumulation during the iterative process, we utilize a novel elastic target bin to replace the original target bin, the width of which is adjusted elastically based on the depth uncertainty. 

---

## Installation

```

conda create -n iebins python=3.8

conda activate iebins

conda install pytorch=1.10.0 torchvision cudatoolkit=11.1

pip install matplotlib, tqdm, tensorboardX, timm, mmcv, open3d

```

## Datasets

You can prepare the datasets KITTI and NYUv2 according to [here](https://github.com/cleinc/bts/tree/master/pytorch) and download the SUN RGB-D dataset from [here](https://rgbd.cs.princeton.edu/), and then modify the data path in the config files to your dataset locations.

## Training

First download the pretrained encoder backbone from [here](https://github.com/microsoft/Swin-Transformer), and then modify the pretrain path in the config files. If you want to train the KITTI_Official model, first download the pretrained encoder backbone from [here](https://drive.google.com/file/d/1qjDnMwmEz0k0XWh7GP2aNPGiAjvOPF_5/view?usp=drive_link), which is provided by [MIM](https://github.com/SwinTransformer/MIM-Depth-Estimation).

Training the NYUv2 model:

```

python iebins/train.py configs/arguments_train_nyu.txt

```

Training the KITTI_Eigen model:

```

python iebins/train.py configs/arguments_train_kittieigen.txt

```

Training the KITTI_Official model:

```

python iebins_kittiofficial/train.py configs/arguments_train_kittiofficial.txt

```

## Evaluation

Evaluate the NYUv2 model:

```

python iebins/eval.py configs/arguments_eval_nyu.txt

```

Evaluate the NYUv2 model on the SUN RGB-D dataset:

```

python iebins/eval_sun.py configs/arguments_eval_sun.txt

```

Evaluate the KITTI_Eigen model:

```

python iebins/eval.py configs/arguments_eval_kittieigen.txt

```

To generate KITTI Online evaluation data for the KITTI_Official model:

```

python iebins_kittiofficial/test.py --data_path path to dataset --filenames_file ./data_splits/kitti_official_test.txt --max_depth 80 --checkpoint_path path to pretrained checkpoint  --dataset kitti --do_kb_crop

```

## Qualitative Depth and Point Cloud Results

You can download the qualitative depth results of [IEBins](https://arxiv.org/abs/2309.14137), [NDDepth](https://arxiv.org/abs/2309.10592), [NeWCRFs](https://openaccess.thecvf.com/content/CVPR2022/html/Yuan_Neural_Window_Fully-Connected_CRFs_for_Monocular_Depth_Estimation_CVPR_2022_paper.html), [PixelFormer](https://openaccess.thecvf.com/content/WACV2023/html/Agarwal_Attention_Attention_Everywhere_Monocular_Depth_Prediction_With_Skip_Attention_WACV_2023_paper.html), [AdaBins](https://openaccess.thecvf.com/content/CVPR2021/html/Bhat_AdaBins_Depth_Estimation_Using_Adaptive_Bins_CVPR_2021_paper.html) and [BTS](https://arxiv.org/abs/1907.10326) on the test sets of NYUv2 and KITTI_Eigen from [here](https://pan.baidu.com/s/1zaFe40mwpQ5cvdDlLZRrCQ?pwd=vfxd) and download the qualitative point cloud results of IEBins, NDDepth, NeWCRFS, PixelFormer, AdaBins and BTS on the NYUv2 test set from [here](https://pan.baidu.com/s/1WwpFuPBGBUaSGPEdThJ6Rw?pwd=n9rw). 

If you want to derive these results by yourself, please refer to the test.py.

If you want to perform inference on a single image, run:

```

python iebins/inference_single_image.py --dataset kitti or nyu --image_path path to image --checkpoint_path path to pretrained checkpoint --max_depth 80 or 10

```

Then you can acquire the qualitative depth result.

## Gradio Demo

1. Install the gradio and other required libraries

```python

pip install gradio gradio_imageslider timm -q

```

2. Run the demo

```python

python notebooks/app.py

```

## Models

| Model | Abs Rel | Sq Rel | RMSE | a1 | a2 | a3| Link|

| ------------ | :---: | :---: | :---: |  :---: |  :---: |  :---: |  :---: |

|NYUv2 (Swin-L)| 0.087 | 0.040 | 0.314 | 0.936 | 0.992 | 0.998 |[[Google]](https://drive.google.com/file/d/14Rn-vxvpXO2EXRaWqCPmh2JufvOurwtl/view?usp=drive_link) [[Baidu]](https://pan.baidu.com/s/1E2KAHtQ-ul99RGp_G7QK1w?pwd=7o4d)|

|NYUv2 (Swin-T)| 0.108 | 0.061 | 0.375 | 0.893 | 0.984 | 0.996 |[[Google]](https://drive.google.com/file/d/1eYkTb3grbDitQ9tJdg1DhAOaGmqgHWHK/view?usp=drive_link) [[Baidu]](https://pan.baidu.com/s/1v5_MJtP0YOSoark9Yw1RaQ?pwd=2k5d)|

|KITTI_Eigen (Swin-L)| 0.050 | 0.142 | 2.011 | 0.978 | 0.998 | 0.999 |[[Google]](https://drive.google.com/file/d/1xaVLDq7zJ-C2GtFvABolSUtK7gzvNQNd/view?usp=drive_link) [[Baidu]](https://pan.baidu.com/s/16mRrKrr9PdZhuZ3ZlkmNlA?pwd=lcjd)|

|KITTI_Eigen (Swin-T)| 0.056 | 0.169 | 2.205 | 0.970 | 0.996 | 0.999 |[[Google]](https://drive.google.com/file/d/1s0LXZmS6_Q4_H_0hmbOldPcVhlRw8Dut/view?usp=drive_link) [[Baidu]](https://pan.baidu.com/s/1xgeqIX5WP5F2MFwypMWV5A?pwd=ygfi)|

| Model | SILog| Abs Rel | Sq Rel | RMSE | a1 | a2 | a3| Link|

| ------------ | :---: | :---: | :---: | :---: |  :---: |  :---: |  :---: |  :---: |

|KITTI_Official (Swinv2-L)| 7.48 | 5.20 | 0.79 | 2.34 | 0.974 | 0.996 | 0.999 |[[Google]](https://drive.google.com/file/d/19ARBiDTIvtZSWJVvhbEWBcZMonXsiOX1/view?usp=drive_link)|

## Citation

If you find our work useful in your research please consider citing our paper:

```

@inproceedings{shao2023IEBins,

title={IEBins: Iterative Elastic Bins for Monocular Depth Estimation},

author={Shao, Shuwei and Pei, Zhongcai and Wu, Xingming and Liu, Zhong and Chen, Weihai and Li, Zhengguo},

booktitle={Advances in Neural Information Processing Systems (NeurIPS)},

year={2023}

}

```

## Contact

If you have any questions, please feel free to contact [email protected].

## Acknowledgement

Our code is based on the implementation of [NeWCRFs](https://github.com/aliyun/NeWCRFs) and [BTS](https://github.com/cleinc/bts). We thank their excellent works.