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Found 2 papers, 1 papers with code
J-MOD$^{2}$: Joint Monocular Obstacle Detection and Depth Estimation
In this work, we propose an end-to-end deep architecture that jointly learns to detect obstacles and estimate their depth for MAV flight applications.
Fast Robust Monocular Depth Estimation for Obstacle Detection with Fully Convolutional Networks
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.
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