18,230 research outputs found
Learning object segmentation from video data
This memo describes the initial results of a project to create a self-supervised algorithm for learning object segmentation from video data. Developmental psychology and computational experience have demonstrated that the motion segmentation of objects is a simpler, more primitive process than the detection of object boundaries by static image cues. Therefore, motion information provides a plausible supervision signal for learning the static boundary detection task and for evaluating performance on a test set. A video camera and previously developed background subtraction algorithms can automatically produce a large database of motion-segmented images for minimal cost. The purpose of this work is to use the information in such a database to learn how to detect the object boundaries in novel images using static information, such as color, texture, and shape. This work was funded in part by the Office of Naval Research contract #N00014-00-1-0298, in part by the Singapore-MIT Alliance agreement of 11/6/98, and in part by a National Science Foundation Graduate Student Fellowship
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes
Unsupervised monocular depth estimation techniques have demonstrated
encouraging results but typically assume that the scene is static. These
techniques suffer when trained on dynamical scenes, where apparent object
motion can equally be explained by hypothesizing the object's independent
motion, or by altering its depth. This ambiguity causes depth estimators to
predict erroneous depth for moving objects. To resolve this issue, we introduce
Dynamo-Depth, an unifying approach that disambiguates dynamical motion by
jointly learning monocular depth, 3D independent flow field, and motion
segmentation from unlabeled monocular videos. Specifically, we offer our key
insight that a good initial estimation of motion segmentation is sufficient for
jointly learning depth and independent motion despite the fundamental
underlying ambiguity. Our proposed method achieves state-of-the-art performance
on monocular depth estimation on Waymo Open and nuScenes Dataset with
significant improvement in the depth of moving objects. Code and additional
results are available at https://dynamo-depth.github.io.Comment: NeurIPS 202
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