154 papers with code • 1 benchmarks • 2 datasets
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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 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.
As a result, we achieve promising results on all datasets and the highest F-Score on the online TNT intermediate benchmark.
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.
While most results in this domain have been achieved on image classification and language modelling problems, here we concentrate on dense per-pixel tasks, in particular, semantic image segmentation using fully convolutional networks.
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.