A CUDA-accelerated high-performance implementation for the structure from motion (SFM) algorithm. Compare to most alternatives, RapidSFM performance is about two orders of magnitude faster.
- Download drone images from https://s3.amazonaws.com/DroneMapper_US/example/DroneMapper_Golf9_May2016.zip (Thank DroneMapper for sharing sample images)
- Extract the image files
- Run:
rsfm -e -d ${folder_of_images}(if the image EXIF contains 35mm equivalent focal length) orrsfm -f ${focal_length_in_pixels} -d ${folder_of_images} - You will find cloud_${num}.{ply/nvm/rsm}, and open *.ply with MeshLab, or *.rsm with the RapidSFM-online Windows client found at https://www.rapidsfm.com
On ubuntu 22.04, you first need to add two PPA repos:
add-apt-repository -y ppa:strukturag/libde265
add-apt-repository -y ppa:strukturag/libheif
Then just install the dependency packages:
apt-get install libboost-dev libboost-test-dev libboost-program-options-dev libboost-fiber-dev libboost-context-dev libboost-filesystem-dev libjpeg-dev libexiv2-dev libturbojpeg0-dev libgtest-dev libeigen3-dev libopencv-dev libyaml-cpp-dev libcereal-dev rapidjson-dev libgeographic-dev cmake libheif-dev cuda
Then just clone this repo, initialize submodules and use the normal cmake build process.
For now, It's only tested on Nvidia Turing (SM_75) and Ampere (SM_86) GPUs but it should work well on Ada (SM_89), too.