The content introduces the SKS and ACA methods for 2D homography decomposition. SKS involves similarity transformations based on anchor points, while ACA includes affine transformations. Both methods offer efficient computation with clear geometric interpretations.
Previous approaches to homography computation are discussed, including algebraic or geometric methods under minimal conditions. The proposed SKS and ACA methods aim to simplify the process with fewer floating-point operations.
SKS decomposes homography into similarity-kernel-similarity transformations, while ACA decomposes it into affine-core-affine transformations. Both methods provide a unified way to handle different planar primitives efficiently.
The FLOPs analysis of the SKS method shows significant speedup compared to traditional homography computation methods like NDLT-SVD, HO-SVD, GPT-LU, and RHO-GE. The proposed methods offer a more efficient approach to 2D homography decomposition.
Source codes for SKS-Homography are available at https://github.com/cscvlab/SKS-Homography.
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arxiv.org
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by Shen Cai,Zha... at arxiv.org 02-29-2024
https://arxiv.org/pdf/2402.18008.pdfDeeper Inquiries