wx-b / midas-cpp

MiDaS-cpp

PyTorch C++ implementation of MiDaS for single-image relative depth prediction. Relative depth prediction, in general, provides more accurate depth prediction in various scene types by forgoing absolute depth scale, compared to absolute depth prediction (e.g., monodepth). For more information, please visit the original work, which is implemented in Python.

The C++ implementation is especially useful for researchers who are working on robotics problems. Tested on Ubuntu 20.04 with an Intel i7 processor and an Nvidia 1070 graphics card.

Personal project that uses MiDaS

(Paper) DeepRelativeFusion: Dense Monocular SLAM using Single-Image Relative Depth Prediction

Tested environment

• Ubuntu 20.04
• Cmake 3.16.3
• Boost 1.71.0
• Python 3.8.5
• OpenCV 3.3.1

Dependencies

• cmake
• Boost
• Python3
• OpenCV

apt install cmake libboost-all-dev python3-dev

As some of you may have a different OpenCV version, we will let you (build and) install your own OpenCV.

Prerequisites

1. Clone the original repo and download the pre-trained model.
2. Download PyTorch C++ (select LibTorch->C++/Java, download the zip file (Pre-cxx11 ABI), and unzip the file in your Home folder).

Convert the pre-trained model to Torch Script

Please see the python folder for further instructions.

Build the project

mkdir build
cd build
cmake ..
make

(Optional) Boost.Python

Uncomment the lines (Line 36 - 42) in CMakelists.txt to build a Boost.Python module. Note that you may have different Boost library version that may result in linking errors.

Run depth prediction

cd build
./midas_inference

(Optional) Boost.Python

cd python
python3 inference.py

Use cases

1. Use MiDaS in a C++ project. You can import the code to your robotics project (e.g., SLAM, visual navigation, AR, etc.).

2. Use MiDaS in a Python project. We also include a Boost.Python module for allowing the model to be used in a Python script.

Licence

The authors take no credit from MiDaS, and therefore the licence(s) should remain intact. Please cite their work if you find them helpful.