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- W4297208082 abstract "Scene reconstruction from unorganized RGB images is an important task in many computer vision applications. Multi-view Stereo (MVS) is a common solution in photogrammetry applications for the dense reconstruction of a static scene. The static scene assumption, however, limits the general applicability of MVS algorithms, as many day-to-day scenes undergo non-rigid motion, e.g., clothes, faces, or human bodies. In this paper, we open up a new challenging direction: dense 3D reconstruction of scenes with non-rigid changes observed from arbitrary, sparse, and wide-baseline views. We formulate the problem as a joint optimization of deformation and depth estimation, using deformation graphs as the underlying representation. We propose a new sparse 3D to 2D matching technique, together with a dense patch-match evaluation scheme to estimate deformation and depth with photometric consistency. We show that creating a dense 4D structure from a few RGB images with non-rigid changes is possible, and demonstrate that our method can be used to interpolate novel deformed scenes from various combinations of these deformation estimates derived from the sparse views." @default.
- W4297208082 created "2022-09-27" @default.
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- W4297208082 date "2019-01-12" @default.
- W4297208082 modified "2023-09-23" @default.
- W4297208082 title "NRMVS: Non-Rigid Multi-View Stereo" @default.
- W4297208082 doi "https://doi.org/10.48550/arxiv.1901.03910" @default.
- W4297208082 hasPublicationYear "2019" @default.
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