FastDepth Implemention in Pytorch

This repo contains Pytorch implementation of depth estimation deep learning network based on the published paper: FastDepth: Fast Monocular Depth Estimation on Embedded Systems

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/FastDepth
cd FastDepth
pip install -r pip_requirements.txt

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

Train

python3 main.py -train -p 100 --epochs 20

Evaluate

python3 main.py --evaluate /home/usr/results/trained_model.pth.tar

Authors

• Sunny Yehuda - sunnyyehuda@gmail.com
• Gil Rafalovich - rafalovichg@gmail.com
• David Sriker - David.Sriker@gmail.com