Unsupervised Monocular Depth Estimation
32 papers with code • 5 benchmarks • 4 datasets
We present a method for jointly training the estimation of depth, ego-motion, and a dense 3D translation field of objects relative to the scene, with monocular photometric consistency being the sole source of supervision.
To tackle this issue, in this paper we propose a novel architecture capable to quickly infer an accurate depth map on a CPU, even of an embedded system, using a pyramid of features extracted from a single input image.
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.
The unsupervised depth estimation is the recent trend by utilizing the binocular stereo images to get rid of depth map ground truth.