2019
DOI: 10.48550/arxiv.1912.04250
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Self-supervised Object Motion and Depth Estimation from Video

Abstract: We present a self-supervised learning framework to estimate the individual object motion and monocular depth from video. We model the object motion as a 6 degree-offreedom rigid-body transformation. The instance segmentation mask is leveraged to introduce the information of object. Compared with methods which predict pixel-wise optical flow map to model the motion, our approach significantly reduces the number of values to be estimated. Furthermore, our system eliminates the scale ambiguity of predictions, thr… Show more

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Cited by 3 publications
(1 citation statement)
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“…Thus, the network can be trained in an unsupervised manner through minimizing the photometric error. Dai et al [127] propose a self-supervised learning framework for jointly estimating individual object motion and depth from monocular video. Instead of modeling the motion by 2D optical flow or 3D scene flow, the object motion is modeled and predicted in the form of full 6 degrees of freedom (6 DoF).…”
Section: B Depth Estimation With Unsupervised Learningmentioning
confidence: 99%
“…Thus, the network can be trained in an unsupervised manner through minimizing the photometric error. Dai et al [127] propose a self-supervised learning framework for jointly estimating individual object motion and depth from monocular video. Instead of modeling the motion by 2D optical flow or 3D scene flow, the object motion is modeled and predicted in the form of full 6 degrees of freedom (6 DoF).…”
Section: B Depth Estimation With Unsupervised Learningmentioning
confidence: 99%