137 papers with code • 8 benchmarks • 15 datasets
The Monocular Depth Estimation is the task of estimating scene depth using a single image.
In this paper, we address the problem of fast depth estimation on embedded systems.
Neural networks have shown great abilities in estimating depth from a single image.
Ranked #1 on Monocular Depth Estimation on Middlebury 2014
To the best of our knowledge, this is the first work to show that deep networks trained using unlabelled monocular videos can predict globally scale-consistent camera trajectories over a long video sequence.
Ranked #21 on Monocular Depth Estimation on KITTI Eigen split
Slow adoption of depth information in the UX layer may be due to the complexity of processing depth data to simply render a mesh or detect interaction based on changes in the depth map.
These methods model depth estimation as a regression problem and train the regression networks by minimizing mean squared error, which suffers from slow convergence and unsatisfactory local solutions.
Ranked #5 on Monocular Depth Estimation on KITTI Eigen split
We show that the proposed method outperforms the state-of-the-art works with significant margin evaluating on challenging benchmarks.
Ranked #4 on Depth Estimation on NYU-Depth V2
We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions.
Ranked #30 on Monocular Depth Estimation on KITTI Eigen split
We propose a novel appearance-based Object Detection system that is able to detect obstacles at very long range and at a very high speed (~300Hz), without making assumptions on the type of motion.
Despite significant progress in monocular depth estimation in the wild, recent state-of-the-art methods cannot be used to recover accurate 3D scene shape due to an unknown depth shift induced by shift-invariant reconstruction losses used in mixed-data depth prediction training, and possible unknown camera focal length.