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| title: IEBins Depth Estimation | |
| emoji: 🏢 | |
| colorFrom: pink | |
| colorTo: pink | |
| sdk: gradio | |
| sdk_version: 4.15.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| <div align="center"> | |
| <h1>IEBins: Iterative Elastic Bins for Monocular Depth Estimation</h1> | |
| <div> | |
| <a href='https://scholar.google.com.hk/citations?hl=zh-CN&user=ecZHSVQAAAAJ' target='_blank'>Shuwei Shao</a><sup>1</sup>  | |
| <a target='_blank'>Zhongcai Pei</a><sup>1</sup>  | |
| <a target='_blank'>Xingming Wu</a><sup>1</sup>  | |
| <a target='_blank'>Zhong Liu</a><sup>1</sup>  | |
| <a href='https://scholar.google.com.hk/citations?hl=zh-CN&user=5PoZrcYAAAAJ' target='_blank'>Weihai Chen</a><sup>2</sup>  | |
| <a href='https://scholar.google.com.hk/citations?hl=zh-CN&user=LiUX7WQAAAAJ' target='_blank'>Zhengguo Li</a><sup>3</sup> | |
| </div> | |
| <div> | |
| <sup>1</sup>Beihang University, <sup>2</sup>Anhui University, <sup>3</sup>A*STAR | |
| </div> | |
| <div> | |
| <h4 align="center"> | |
| • <a href="https://arxiv.org/abs/2309.14137" target='_blank'>NeurIPS 2023</a> • | |
| </h4> | |
| </div> | |
| [](https://www.cvlibs.net/datasets/kitti/eval_depth.php?benchmark=depth_prediction) | |
| [](umuthopeyildirim/IEBins-Depth-Perception) | |
| ## Abstract | |
| <div style="text-align:center"> | |
| <img src="assets/teaser.jpg" width="80%" height="80%"> | |
| </div> | |
| </div> | |
| <strong>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. </strong> | |
| --- | |
| </div> | |
| ## 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 | |
| ``` | |
| ## Gradio Demo | |
| 1. Install the gradio and other required libraries | |
| ```python | |
| pip install gradio gradio_imageslider timm -q | |
| ``` | |
| 2. Run the demo | |
| ```python | |
| python app.py | |
| ``` | |
| ## 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. | |
| ## 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 swshao@buaa.edu.cn. | |
| ## 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. | |