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- W4313477588 abstract "We present MosaicMVS, a novel learning-based depth estimation framework for a mosaic-based omnidirectional multi-view stereo (MVS) camera setup. It uses a regular field of view (FOV) MVS network for an omnidirectional imaging setup with explicit consideration of hypothetical voxel-wise FOV overlaps. The resulting depth predictions are accurate and agree on the omnidirectional multi-view geometry. Unlike existing MVS setups, MosaicMVS camera setup can be easily applied to omnidirectional indoor scenes without having to account for con-straints such as intricate epipolar constraints and the distortion of omnidirectional cameras. We validate the effectiveness of our framework on a new challenging indoor dataset in terms of depth estimation, reconstruction, and view synthesis. We also present new evaluation metric to check reconstruction performance using post-processed masks for accurate evaluation without any ground truth depth map or laser-scanned reconstructions. Experimental results show that our framework outperforms the state-of-the-art MVS methods in a large margin in all test scenes." @default.
- W4313477588 created "2023-01-06" @default.
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- W4313477588 date "2023-01-01" @default.
- W4313477588 modified "2023-09-24" @default.
- W4313477588 title "MosaicMVS: Mosaic-based Omnidirectional Multi-view Stereo for Indoor Scenes" @default.
- W4313477588 doi "https://doi.org/10.1109/tmm.2022.3232239" @default.
- W4313477588 hasPublicationYear "2023" @default.
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