129 papers with code • 1 benchmarks • 1 datasets
We show that the proposed method outperforms the state-of-the-art works with significant margin evaluating on challenging benchmarks.
We consider the problem of dense depth prediction from a sparse set of depth measurements and a single RGB 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.
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.
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture
In this paper we address three different computer vision tasks using a single basic architecture: depth prediction, surface normal estimation, and semantic labeling.