In this post I’d like to introduce my GPU-Surf implementation and a new library for Ogre3D Ogre::GPGPU.
What is GPUSurf ?
It is a GPU accelerated version of Surf algorithm based on a paper of Nico Cornelis1.
This version is using GPGPU technique (pixel shader) and Cuda for computing. The Cuda part was done using my Ogre::Cuda library and the GPGPU part was done using a new library called Ogre::GPGPU. This new library is just a little helper that hide the fact that GPGPU computing is done using quad rendering.
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| Screenshot of my GPU-Surf implementation (3 octaves displayed) |
GPU-Surf could be used to help panoramic image creation, create tracking algorithm, speed-up structure from motion… I’m currently using SiftGPU to speed-up image matching step of structure from motion tools (bundler), but SiftGPU v360 as a memory issue (it’s eating a lot of virtual memory, your program as to be 64bit to bypass common 32bit application limitation: 2Go of virtual memory under windows), and Sift matching is more expensive than Surf (descriptor of 128 float vs 64 for Surf). That’s why I have decided to create my own implementation of GPU-Surf.
| Structure from motion using PhotoSynth |
Implementation details:
The current version of my implementation of GPU-Surf is uncomplete (descriptor missing, only detector is available). You’ll get all information about license (MIT), svn repository, demo (GPUSurfDemo1.zip) and documentation on the GPUSurf page.
[1]: N. Cornelis, L. Van Gool: Fast Scale Invariant Feature Detection and Matching on Programmable Graphics Hardware (ncorneli_cvpr2008.pdf).
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