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Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction
| Date | Stars |
|---|---|
| 2026-07-24 | 358 |
| 2026-07-25 | 358 |
| 2026-07-28 | 358 |
| 2026-07-30 | 358 |
| 2026-08-06 | 358 |
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# Introduction
This repo implements the system described in the CVPR-2018 paper:
[**Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction**
](https://arxiv.org/abs/1803.03893)
Huangying Zhan, Ravi Garg, Chamara Saroj Weerasekera, Kejie Li, Harsh Agarwal, Ian Reid
```
@InProceedings{Zhan_2018_CVPR,
author = {Zhan, Huangying and Garg, Ravi and Saroj Weerasekera, Chamara and Li, Kejie and Agarwal, Harsh and Reid, Ian},
title = {Unsupervised Learning of Monocular Depth Estimation and Visual Odometry With Deep Feature Reconstruction},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}
```
This repo includes (1) the training procedure of our models; (2) evaluation scripts for the results; (3) trained models and results.
### Contents
1. [Requirements](#part-1-requirements)
2. [Prepare dataset](#part-2-prepare-dataset)
3. [Depth](#part-3-depth)
4. [Depth and odometry](#part-4-depth-and-odometry)
5. [Feature Reconstruction Loss for Depth](#part-5-feature-reconstruction-loss-for-depth)
6. [Depth, odometry and feature](#part-6-depth-odometry-and-feature)
7. [Result evaluation](#part-7-result-evaluation)
### Part 1. Requirements
This code was tested with Python 2.7, CUDA 8.0 and Ubuntu 14.04 using [Caffe](http://caffe.berkeleyvision.org/).
Caffe: Add the required layers in `./caffe` into your own Caffe. Remember to enable Python Layers in the Caffe configuration.
Most of our required models, trained models and results can be downloaded from [here](https://www.dropbox.com/sh/qxfqflrrzzwupua/AAAPA1mF0QaKwwR2Ds0jtDhYa?dl=0). The following instruction also includes specific links to the items.
### Part 2. Download dataset and models
The main dataset used in this project is [KITTI Driving Dataset](http://www.cvlibs.net/datasets/kitti/raw_data.php). Please follow the instruction in `./data/README.md` to prepare the required dataset.
For our trained models and pre-requested models, please visit [here](https://www.dropbox.com/sh/60onn52jm9g2ygu/AADUkDRkwycS1STazstG5XOpa?dl=0) to download the models and put the models into the directory `./models`.
### Part 3. Depth
In this part, the training of single view depth estimation network from stereo pairs is introduced. Photometric loss is used as the main supervision signal. Only stereo pairs are used in this experiment.
1. Update `$YOUR_CAFFE_DIR` in `./experiments/depth/train.sh`.
2. Run `bash ./expriments/depth/train.sh`.
The trained models are saved in `./snapshots/depth`
### Part 4. Depth and odometry
In this part, the joint training of the depth estimation network and the visual odometry network is introduced.
Photometric losses for spatial pairs and temporal pairs are used as the main supervision signal.
Both spatial (stereo) pairs and temporal pairs (i.e. stereo sequences) are used in this experiment.
To facilitate the training, the model trained in the Depth experiment is used as an initialization.
1. Update `$YOUR_CAFFE_DIR` in `./experiments/depth_odometry/train.sh`.
2. Run `bash ./expriments/depth_odometry/train.sh`.
The trained models are saved in `./snapshots/depth_odometry`
### Part 5. Feature Reconstruction Loss for Depth
In this part, the training of single view depth estimation network from stereo pairs is introduced. Both photometric loss and feature reconstruction loss are used as the main supervision signal. Only stereo pairs are used in this experiment. There are several features we have tried for this experiment. Currently, only the example of using **KITTI Feat.** is shown here. More details of using other features will be updated later.
To facilitate the training, the model trained in the Depth experiment is used as an initialization.
1. Update `$YOUR_CAFFE_DIR` in `./experiments/depth_feature/train.sh`.
2. Run `bash ./expriments/depth_feature/train.sh`.
The trained models are saved in `./snapshots/depth_feature`
### Part 6.Excerpt of 7,297 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c843b25ac617cf3b, topic:computer-vision, readme:computer vision, desc:depth estimation
matched fp:c843b25ac617cf3b, topic:deep-learning