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- W4384644382 abstract "Recent work in unsupervised multi-object segmentation shows impressive results by predicting motion from a single image despite the inherent ambiguity in predicting motion without the next image. On the other hand, the set of possible motions for an image can be constrained to a low-dimensional space by considering the scene structure and moving objects in it. We propose to model pixel-wise geometry and object motion to remove ambiguity in reconstructing flow from a single image. Specifically, we divide the image into coherently moving regions and use depth to construct flow bases that best explain the observed flow in each region. We achieve state-of-the-art results in unsupervised multi-object segmentation on synthetic and real-world datasets by modeling the scene structure and object motion. Our evaluation of the predicted depth maps shows reliable performance in monocular depth estimation." @default.
- W4384644382 created "2023-07-19" @default.
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- W4384644382 date "2023-07-16" @default.
- W4384644382 modified "2023-09-27" @default.
- W4384644382 title "Multi-Object Discovery by Low-Dimensional Object Motion" @default.
- W4384644382 doi "https://doi.org/10.48550/arxiv.2307.08027" @default.
- W4384644382 hasPublicationYear "2023" @default.
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