DenseDepth Implemention in Pytorch

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

Installation

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

Requirements

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.

Step-by-Step Procedure

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

NYU database

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

Training the model

Execute training by running:

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

Testing the model

Execute testing by running:

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

Authors

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