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We present a novel approach for unsupervised learning of depth and ego-motion from monocular video.
We present an approach which takes advantage of both structure and semantics for unsupervised monocular learning of depth and ego-motion.
We present a novel method for simultaneous learning of depth, egomotion, object motion, and camera intrinsics from monocular videos, using only consistency across neighboring video frames as supervision signal.
Learning based methods have shown very promising results for the task of depth estimation in single images.
#9 best model for Monocular Depth Estimation on KITTI Eigen split
Per-pixel ground-truth depth data is challenging to acquire at scale.
This paper addresses the problem of estimating the depth map of a scene given a single RGB image.
According to this depth estimate, our framework then maps the input image to a point cloud and synthesizes the resulting video frames by rendering the point cloud from the corresponding camera positions.
SOTA for Depth Estimation on NYU-Depth V2