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Learning based methods have shown very promising results for the task of depth estimation in single images.
#6 best model for Monocular Depth Estimation on KITTI Eigen split
Per-pixel ground-truth depth data is challenging to acquire at scale.
#4 best model for Monocular Depth Estimation on KITTI Eigen split
Accurate depth estimation from images is a fundamental task in many applications including scene understanding and reconstruction.
Many standard robotic platforms are equipped with at least a fixed 2D laser range finder and a monocular camera.
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 learning based methods showing promising results in single view depth estimation and visual odometry, most existing approaches treat the tasks in a supervised manner.
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.
#11 best model for Monocular Depth Estimation on KITTI Eigen split
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.
We present a generalization of the Cauchy/Lorentzian, Geman-McClure, Welsch/Leclerc, generalized Charbonnier, Charbonnier/pseudo-Huber/L1-L2, and L2 loss functions.