Multi-Object Discovery by Low-Dimensional Object Motion
Sadra Safadoust Fatma Güney
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.