Object-level Geometric Structure Preserving for Natural Image Stitching
CoRR(2024)
摘要
The topic of stitching images with globally natural structures holds
paramount significance. Current methodologies exhibit the ability to preserve
local geometric structures, yet fall short in maintaining relationships between
these geometric structures. In this paper, we endeavor to safeguard the
overall, OBJect-level structures within images based on Global Similarity
Prior, while concurrently mitigating distortion and ghosting artifacts with
OBJ-GSP. Our approach leverages the Segment Anything Model to extract geometric
structures with semantic information, enhancing the algorithm's ability to
preserve objects in a manner that aligns more intuitively with human
perception. We seek to identify spatial constraints that govern the
relationships between various geometric boundaries. Recognizing that multiple
geometric boundaries collectively define complete objects, we employ triangular
meshes to safeguard not only individual geometric structures but also the
overall shapes of objects within the images. Empirical evaluations across
multiple image stitching datasets demonstrate that our method establishes a new
state-of-the-art benchmark in image stitching. Our implementation and dataset
is publicly available at https://github.com/RussRobin/OBJ-GSP .
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