Multi-Object Discovery by Low-Dimensional Object Motion

• URL: http://arxiv.org/abs/2307.08027v1
• Date: Sun, 16 Jul 2023 12:35:46 GMT
• Title: Multi-Object Discovery by Low-Dimensional Object Motion
• Authors: Sadra Safadoust, Fatma G\"uney
• Abstract summary: We propose to model pixel-wise geometry and object motion to remove ambiguity in reconstructing flow from a single image. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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