Depth estimation

This project compares performance of several depth estimation methods:

• Traditional stereo depth estimation methods - Block Matching (implemented from scratch) and Semi-Global Block Matching (opencv implementation)
• HitNet Neural Network for stereo depth estimation
• Depth Anything Neural Network for monocular depth estimation

The results can be found in ZPO.pdf file.

Team members

• Josef Kotoun
• Jiří Vlasák
• Vít Tlustoš

Installation

Install requirements with pip install -r requirements.txt

Usage

To see predictions on example image, run python demo.py. The script will display the disparity map for the example image for all of the models.

To run the DepthAnything model on input image, run python run.py --img img.png. The script will display the predicted depth map for the input image.

To evaluate the models run the predict.py script. It expects path to the dataset, patterns for left and right images and ground truth disparity maps. Patterns for ground truth are not needed if the --evaluate flag is not set. To evaluate the models on the Middlebury dataset, download the dataset and ground truths from http://vision.middlebury.edu/stereo/submit3/ and set the --data_pattern to the path of the dataset. The script can be used to evaluate the following models:

EXAMPLE USAGE:

SMGB 
python predict.py --data_pattern="data/ --iml_pattern="**\im0.png" --imr_pattern="**\im1.png" --gtl_pattern="**/disp0GT.pfm" --evaluate --predictor="sgbm" 

BM
python predict.py --data_pattern="data/" --iml_pattern="**\im0.png" --imr_pattern="**\im1.png" --gtl_pattern="**/disp0GT.pfm" --evaluate --predictor="bm"

HITNET
python predict.py --data_pattern="data/" --iml_pattern="**\im0.png" --imr_pattern="**\im1.png" --gtl_pattern="**/disp0GT.pfm" --evaluate --predictor="hitnet" --model_path="models/flyingthings_finalpass_xl.pb"