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STereo TRansformer (STTR)

This is the official repo for our work Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers.

Fine-tuned result on street scene:

Generalization to medical domain when trained only on synthetic data:

If you find our work relevant, please cite

@InProceedings{Li_2021_ICCV,
    author    = {Li, Zhaoshuo and Liu, Xingtong and Drenkow, Nathan and Ding, Andy and Creighton, Francis X. and Taylor, Russell H. and Unberath, Mathias},
    title     = {Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective With Transformers},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6197-6206}
}

Update

• 2022.01.16: Resolved torch version compatibility issue in Issue #8. A big thanks to @DeH40!
• 2021.03.29: Added code/instruction to obtain training data from Scene Flow.
• 2021.01.13: STTR-light is released. Use branch sttr-light for the new model.
• 2020.11.05: First code and arxiv release

Table of Content

• Introduction
  • Benefits of STTR
  • Working Theory
• Dependencies
• Pre-trained Models
• Folder Structure
  • Code
  • Data
• Usage
  • Colab/Notebook
  • Terminal Example
• Expected Result
• Common Q&A
• License
• Contributing
• Acknowledgement

Introduction

Benefits of STTR

STereo TRansformer (STTR) revisits stereo depth estimation from a sequence-to-sequence perspective. The network combines conventional CNN feature extractor and long-range relationship capturing module Transformer. STTR is able to relax prior stereo depth estimation networks in three aspects:

• Disparity range naturally scales with image resolution, no more manually set range.
• Explicit occlusion handling.
• Imposing uniqueness constraint.

STTR performs comparably well against prior work with refinement in Scene Flow and KITTI 2015. STTR is also able to generalize to MPI Sintel, KITTI 2015, Middlebury 2014 and SCARED when trained only on synthetic data.

Working Theory

Attention

Two types of attention mechanism are used: self-attention and cross-attention. Self-attention uses context within the same image, while cross-attention uses context across two images. The attention shrinks from global context to local context as the layer goes deeper. Attention in a large textureless area tends to keep attending dominant features like edges, which helps STTR to resolve ambiguity.

Self-Attention

Cross-Attention

Relative Positional Encoding

We find that only image-image based attention is not enough. Therefore, we opt in relative positional encoding to provide positional information. This allows STTR to use the relative distance from a featureless pixel to dominant pixel (such as edge) to resolve ambiguity. In the following example, STTR starts to texture the center of the table using relative distance, thus strides parallel to the edges start to show.

Feature Descriptor

Implicit Learnt Feature Classification

We observe that the feature extractor before Transformer actually learns without any explicit supervision to classify pixels into two clusters - textured and textureless. We hypothesize that this implicit learnt classification helps STTR to generalize.

Implicit Learnt Classification

Dependencies

We recommend the following steps to set up your environment

• Create your python virtual environment by

  conda create --name sttr python=3.6 # create a virtual environment called "sttr" with python version 3.6
  

  (Python version >= 3.6)

• Install Pytorch: Please follow link here.

  (PyTorch version >= 1.5.1)

• Other third-party packages: You can use pip to install the dependencies by

  pip install -r requirements.txt
  

• (Optional) Install Nvidia apex: We use apex for mixed precision training to accelerate training. To install, please follow instruction here

  • You can remove apex dependency if
    • you have more powerful GPUs, or
    • you don't need to run the training script.
  • Note: We tried to use the native mixed precision training from official Pytorch implementation. However, it currently does not support gradient checkpointing for LayerNorm. We will post update if this is resolved.

Pre-trained Models

You can download the pretrained model from the following links.

• Google Drive:

• Baidu download link:
  • Link: https://pan.baidu.com/s/1euozEOX3fVdYID3v5dHJ_w
  • Password: jvda

Folder Structure

Code Structure

stereo-transformer
    |_ dataset (dataloder)
    |_ module (network modules, including loss)
    |_ utilities (training, evaluation, inference, logger etc.)

Data Structure

Please see sample_data folder for details. We keep the original data folder structure from the official site. If you need to modify the existing structure, make sure to modify the dataloader.

• Note: We only provide one sample of each dataset to run the code. We do not own any copyright or credits of the data.

Scene Flow

SCENE_FLOW
    |_ RGB_finalpass
        |_ TRAIN
            |_ A
                |_0000
    |_ disparity
        |_ TRAIN
            |_ A
                |_0000
    |_ occlusion
        |_ TRAIN
            |_ left

MPI Sintel

MPI_Sintel
    |_ training
        |_ disparities
        |_ final_left 
        |_ final_right 
        |_ occlusions (occlusions of left border of objects)
        |_ outofframe (occlusion of left border of images)

KITTI 2015

KITTI_2015
    |_ training
        |_ disp_occ_0 (disparity including occluded region)
        |_ image_2 (left image)
        |_ image_3 (right image)

MIDDLEBURY_2014

MIDDLEBURY_2014
    |_ trainingQ
        |_ Motorcycle (scene name)
            |_ disp0GT.pfm (left disparity)
            |_ disp1GT.pfm (right disparity)
            |_ im0.png (left image)
            |_ im1.png (right image)
            |_ mask0nocc.png (left occlusion)
            |_ mask1nocc.png (right occlusion)

SCARED

SCARED
    |_ training
        |_ disp_left
        |_ img_left 
        |_ img_right
        |_ occ_left 

Usage

Colab/Notebook Example

If you don't have a GPU, you can use Google Colab:

• An example of how to run inference is given in the Colab example <img src="https://colab.research.google.com/assets/colab-badge.svg" align="center">

If you have a GPU and want to run locally:

• Download pretrained model using links in Pre-trained Models.
  • Note: The pretrained model is assumed to be in the stereo-transformer folder.
• An example of how to run inference is given in file inference_example.ipynb.

Terminal Example

• Download pretrained model using links in Pre-trained Models.
• Run pretraining by

  sh scripts/pretrain.sh
  

  • Note: please set the --dataset_directory argument in the .sh file to where Scene Flow data is stored, i.e. replace PATH_TO_SCENEFLOW
• Run fine-tune on KITTI by

  sh scripts/kitti_finetune.sh
  

  • Note: please set the --dataset_directory argument in the .sh file to where KITTI data is stored, i.e. replace PATH_TO_KITTI
  • Note: the pretrained model is assumed to be in the stereo-transformer folder.
• Run evaluation on the provided KITTI example by

  sh scripts/kitti_toy_eval.sh
  

  • Note: the pretrained model is assumed to be in the stereo-transformer folder.

Expected Result

The result of STTR may vary by a small fraction depending on the trial, but it should be approximately the same as the tables below.

Expected result of STTR (sceneflow_pretrained_model.pth.tar) and STTR-light (sttr_light_pretrained_model.pth.tar).

Sceneflow

Generalization without fine-tuning.

Expected 3px error result of kitti_finetuned_model.pth.tar

Common Q&A

1. I don't see occlusion from Scene Flow dataset. What should I do?
   Scene Flow dataset can be downloaded at https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html. However, you may notice that the Full datasets has disparity and images, but not occlusion. What you need to do is to download the occlusion from the DispNet/FlowNet2.0 dataset subsets and use the provided training list on the right named train to only use the subset of Full datasets with the occlusion data. Please see utilities/subsample_sceneflow.py for more details of subsampling the Full datasets.

2. How much memory does it require to train/inference?
   We provide a flexible design to accommodate different hardware settings.

   • For both training and inference, you change the downsample parameter to reduce memory consumption at the cost of potential performance degradation.
   • For training, you can always change the crop size in dataset/scene_flow.py.
   • For both training and inference, you can use the light-weight model STTR-light.

3. What are occluded regions?
   "Occlusion" means pixels in the left image do not have a corresponding match in right images. Because objects in right image are shifted to the left a little bit compared to the right image, thus pixels in the following two regions generally do not have a match:

   • At the left border of the left image
   • At the left border of foreground objects

4. Why there are black patches in predicted disparity with values 0?
   The disparities of occluded region are set to 0.

5. Why do you read disparity map including occluded area for KITTI during training?
   We use random crop as a form of augmentation, thus we need to recompute occluded regions again. The code for computing occluded area can be found in dataset/preprocess.py.

6. How to reproduce feature map visualization in Figure 4 of the paper?
   The feature map is taken after the first LayerNorm in Transformer. We use PCA trained on the first and third layer to reduce the dimensionality to 3.

License

This project is under the Apache 2.0 license. Please see LICENSE for more information.

Contributing

We try out best to make our work easy to transfer. If you see any issues, feel free to fork the repo and start a pull request.

Acknowledgement

Special thanks to authors of SuperGlue, PSMNet and DETR for open-sourcing the code. We also thank GwcNet, GANet, Bi3D, AANet for open-sourcing the code. We thank Xiran for MICCAI pre-processing.