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Per-pixel ground-truth depth data is challenging to acquire at scale.
Accurate depth estimation from images is a fundamental task in many applications including scene understanding and reconstruction.
#3 best model for Monocular Depth Estimation on NYU-Depth V2
In this paper, we address the problem of fast depth estimation on embedded systems.
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.
#2 best model for 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.
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.
#13 best model for Monocular Depth Estimation on KITTI Eigen split (using extra training data)
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.
#17 best model for Monocular Depth Estimation on KITTI Eigen split
Despite learning based methods showing promising results in single view depth estimation and visual odometry, most existing approaches treat the tasks in a supervised manner.
Experimental results show that these two improvements enable to attain higher accuracy than the current state-of-the-arts, which is given by finer resolution reconstruction, for example, with small objects and object boundaries.
#8 best model for Monocular Depth Estimation on NYU-Depth V2