Stereo Matching algorithms reconstruct a depth map from a pair of stereoscopic images. Stereo Matching algorithms are computationally intensive. Implementing efficient stereo matching algorithms on embedded systems is very challenging. This paper compares implementation efficiency and output quality of the state of the art dense stereo matching algorithms on the same multicore embedded system. The three different classes of stereo matching algorithms are local methods, semi-global methods and global methods. This paper compares three algorithms of the literature with a good trade-off between complexity and accuracy : Bilateral Filtering Aggregation (BFA, Local Method), One Dimension Belief Propagation (BP-1D, Semi Global Methods) and Semi Global Matching (SGM, Semi Global Methods). For the same input data the BFA, BP-1D and SGM were fully optimized and parallelized on the C6678 platform and run at respectively 10.7 ms, 4.1 ms and 47.1 ms.
Judicaël Menant, Jean-François Nezan, Luce Morin, Muriel Pressigout, "A comparison of stereo matching algorithms on multi-core Digital Signal Processor platform" in Proc. IS&T Int’l. Symp. on Electronic Imaging: 3D Image Processing, Measurement (3DIPM), and Applications, 2017, pp 49 - 54, https://doi.org/10.2352/ISSN.2470-1173.2017.20.3DIPM-007