This repo contains Pytorch implementation of depth estimation deep learning network based on the published paper: High Quality Monocular Depth Estimation via Transfer Learning

This repository was part of the "Autonomous Robotics Lab" in Tel Aviv University

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

This code was tested with:

• Ubuntu 18.04 with python 3.6.9

The code also runs on Jetson TX2, for which all dependencies need to be installed via NVIDIA JetPack SDK.

In order to set the virtual environment, apriori installation of Anaconda3 platform is required.

Use the following commands to create a new working virtual environment with all the required dependencies.

GPU based enviroment:

git clone https://github.com/tau-adl/DenseDepth
cd DenseDepth
pip install -r pip_requirements.txt

In order to utilize the GPU implementation make sure your hardware and operation system are compatible for Pytorch with python 3

Download the preprocessed NYU Depth V2 dataset in HDF5 format and place it under a data folder outside the repo directory. The NYU dataset requires 32G of storage space.

./DataCollect.sh

Execute training by running:

python3 Train.py --epochs 10 --lr 0.01

Execute testing by running:

python3 Evaluate.py --path TrainedModel/EntireModel/model_batch_10_epochs_1.pt

• David Sriker - David.Sriker@gmail.com
• Sunny Yehuda - sunnyyehuda@gmail.com