https://github.com/ashok-arjun/monocular-depth-estimation
Depth estimation from single-view RGB images
https://github.com/ashok-arjun/monocular-depth-estimation
computer-vision deep-learning depth-estimation machine-learning monocular-depth-estimation pytorch research
Last synced: 10 months ago
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Depth estimation from single-view RGB images
- Host: GitHub
- URL: https://github.com/ashok-arjun/monocular-depth-estimation
- Owner: ashok-arjun
- License: mit
- Created: 2020-07-09T18:25:48.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2022-11-22T09:48:35.000Z (over 3 years ago)
- Last Synced: 2023-03-05T07:42:45.108Z (about 3 years ago)
- Topics: computer-vision, deep-learning, depth-estimation, machine-learning, monocular-depth-estimation, pytorch, research
- Language: Python
- Homepage:
- Size: 56.1 MB
- Stars: 5
- Watchers: 2
- Forks: 1
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
[](https://zenodo.org/badge/latestdoi/278440645)
# Perceptual Dense Network for High-Quality Monocular Depth Estimation
Here, we propose an approach that integrates learning **low level** and **high level features** to estimate **high-quality depth maps** from **single-view 2-D images**
A deep **fully convolutional architecture** and suitable optimization objectives that minimize **a set of per-pixel loss functions** and **a perceptual loss function**, along with augmentation and training strategies has been employed.
# Results
## Qualitative
| Input RGB Image | Ground truth depth map | Our results|
|:---------------:|:----------------------:|:----------:|
||||
||||
## Quantitative
|
| 
|
|
|
|
| :--------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------:| :--------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------:
|0.852 | 0.976 | 0.995 | 0.122 | 0.500 | 0.053
# Instructions
Installation
To install, execute
```
pip install -r requirements.txt
```
Data
[NYU Depth v2 train](https://tinyurl.com/nyu-data-zip) - (50K images) (4.1 GB)
On extraction, there will be a ```data``` folder.
[NYU Depth v2 test](https://s3-eu-west-1.amazonaws.com/densedepth/nyu_test.zip) - (654 images) (1 GB)
On extraction, there will be three ```.npy``` files.
Training
The script ```train.py``` contains the code for training the model. It can be invoked with the following arguments:
```
usage: train.py [-h] --train_dir TRAIN_DIR --test_dir TEST_DIR --batch_size
BATCH_SIZE --checkpoint_dir CHECKPOINT_DIR --epochs EPOCHS
[--checkpoint CHECKPOINT] [--lr LR]
[--log_interval LOG_INTERVAL] [--backbone BACKBONE]
[--test_batch_size TEST_BATCH_SIZE]
[--perceptual_weight PERCEPTUAL_WEIGHT]
Training of depth estimation model
-h, --help show this help message and exit
mandatory arguments:
--train_dir TRAIN_DIR
Train directory path - should contain the 'data'
folder
--test_dir TEST_DIR Test directory path - should contain 3 files
--batch_size BATCH_SIZE
Batch size to process the train data
--checkpoint_dir CHECKPOINT_DIR
Directory to save checkpoints in
--epochs EPOCHS Number of epochs
optional arguments:
--checkpoint CHECKPOINT
Model checkpoint path
--lr LR Learning rate
--log_interval LOG_INTERVAL
Interval to print the avg. loss and metrics
--backbone BACKBONE Model backbone: densenet161 or densenet121
--test_batch_size TEST_BATCH_SIZE
Batch size for frequent testing
--perceptual_weight PERCEPTUAL_WEIGHT
Weight for the perceptual loss
```
It is advised to run the code on a GPU. The code automatically detects if a GPU is available, and uses it.
Evaluation/Inference
The script ```evaluate.py``` contains the code for evaluating the model/for predicting the depth given an image. It can be invoked with the following arguments:
```
usage: evaluate.py [-h] --model MODEL [--data_dir DATA_DIR] [--img IMG]
[--batch_size BATCH_SIZE] [--output_dir OUTPUT_DIR]
[--backbone BACKBONE]
Evaluation of depth estimation model on either test data/own images
-h, --help show this help message and exit
arguments:
--model MODEL Model checkpoint path
--data_dir DATA_DIR Test data directory(If evaluation on test data)
--img IMG Image path(If evaluation on a single image)
--batch_size BATCH_SIZE
Batch size to process the test data
--output_dir OUTPUT_DIR
Directory to save output depth images
--backbone BACKBONE Model backbone - densenet 121 or densenet 161
```
It is advised to run the code on a GPU. The code automatically detects if a GPU is available, and uses it.
# Citation
Please cite the following if you find the code useful in your research:
```
@misc{Ashok2020,
author = {Ashok, Arjun},
title = {Perceptual Dense Network for High-Quality Monocular Depth Estimation},
year = {2020},
publisher = {Zenodo},
doi = {10.5281/zenodo.4041690},
version = {1.0},
url = {https://doi.org/10.5281/zenodo.4041690}
}
```