In this project, we extend direct sparse odometry(DSO) with a self-supervised depth estimation module named packnet-sfm.

• libtorch(c++11)
• OpenCV3+
• CUDA(10.1)
• Protobuf >= 3.8.x

TensorRT is one the applicable inference framework which reduces the consumed inference time and computation overhead greatly. Although converting model into torchscript is also a good choice to save time, the builtin optimization principles of tensorrt are still much effcient than torchscript. I also trying to optimize this depth estimaition module with TensorRT.

• ONNX(1.6.0)
• TensorRT 7.0 open source libaries (master branch)

• prepare all required libs mentioned before, and download this projet.

         git clone https://github.com/TengFeiHan0/deepDSO.git 
  

• go to monodepth2.cpp and download the required torchscript model(packnet.pt) and another required lib (libtorch) from its offical website

• Build

    cd deepDSO
    mkdir build
    cd build
    cmake ..
    make -j4