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Code for "Real-time self-adaptive deep stereo" - CVPR 2019 (ORAL)
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# Real-Time Self-Adaptive Deep Stereo
💡 **UPDATE!** Code for [Continual Adaptation for Deep Stereo](https://arxiv.org/pdf/2007.05233.pdf), accepted to **TPAMI**, is now available! Details under the 💡 **UPDATE!** tags
Code for [Real-time self-adaptive deep stereo](https://arxiv.org/abs/1810.05424), accepted as **ORAL** at **CVPR 2019**.
| [CVPR paper](https://arxiv.org/abs/1810.05424) | [CVPR video](https://www.youtube.com/watch?v=7SjyzDxmCY4) | [CVPR_Live_Demo](https://www.youtube.com/watch?v=4O-7OzVYAeU) |
| [TPAMI paper](https://arxiv.org/pdf/2007.05233.pdf) | [TPAMI video](https://www.youtube.com/watch?v=YnPGbQE2dLQ) |
| [ -Unofficial- Keras reimplementation by ChristianOrr](https://github.com/ChristianOrr/madnet-deep-stereo-with-keras) |
**Abstract**:
Deep convolutional neural networks trained end-to-end are the undisputed state-of-the-art methods to regress dense disparity maps directly from stereo pairs. However, such methods suffer from notable accuracy drops when exposed to scenarios significantly different from those seen in the training phase (e.g.real vs synthetic images, indoor vs outdoor, etc). As it is unlikely to be able to gather enough samples to achieve effective training/ tuning in any target domain, we propose to perform unsupervised and continuous online adaptation of a deep stereo network in order to preserve its accuracy independently of the sensed environment. However, such a strategy can be extremely demanding regarding computational resources and thus not enabling real-time performance. Therefore, we address this side effect by introducing a new lightweight, yet effective, deep stereo architecture Modularly ADaptive Network (MADNet) and by developing Modular ADaptation (MAD), an algorithm to train independently only sub-portions of our model. By deploying MADNet together with MAD we propose the first ever realtime self-adaptive deep stereo system.
If you use this code please cite:
```
@InProceedings{Tonioni_2019_CVPR,
author = {Tonioni, Alessio and Tosi, Fabio and Poggi, Matteo and Mattoccia, Stefano and Di Stefano, Luigi},
title = {Real-time self-adaptive deep stereo},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
@article{Poggi2021continual,
author={Poggi, Matteo and Tonioni, Alessio and Tosi, Fabio
and Mattoccia, Stefano and Di Stefano, Luigi},
title={Continual Adaptation for Deep Stereo},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
year={2021}
}
```
## Requirements
This software has been tested with python3 and tensorflow 1.10. All required packages can be installed using pip and requirements.txt
```
pip3 install -r requirements.txt
```
## Pretrained Weights for Network
Pretrained weights for both DispNet and MADNet available [here](https://drive.google.com/open?id=1GwGxBOFx-NlUo9RAUgPlgPvaHCpGedlm).
## Online Adaptation Step by step on Video Sequences
1. Create a csv file for your video sequence similar to `example_list.csv`.
Each row should contain absolute paths to the input data in the following order:
"*path_to_left_rgb*,*path_to_right_rgb*,*path_to_groundtruth*"
Ground truth data will only be used to compute the network performance not for the online adaptation.
💡 **UPDATE!** To run the TPAMI code, the csv file should contain absolute path to proxy disparities as well:
"*path_to_left_rgb*,*path_to_right_rgb*,*path_to_groundtruth*,*path_to_proxies*"
2. Download pretrained network from [here](https://drive.google.com/open?id=1GwGxBOFx-NlUo9RAUgPlgPvaHCpGedlm).
3. Perform different kinds of online adaptation with `Stereo_Online_Adaptation.py`, to list all available options use `python3 Stereo_Online_Adaptation.py -h`.
Example of online adaptation using MADNet and MAD:
```bash
LIST="path/to/the/list/of/frames/" #the one described at step (1)
OUTPUT="patExcerpt of 7,178 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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