@inproceedings{koltun2016dense,
author = {Koltun, Vladlen and Chen, Qifeng and Vineet, Vibhav and Ranftl, Rene},
title = {Dense Monocular Depth Estimation in Complex Dynamic Scenes},
year = {2016},
month = {April},
abstract = {We present an approach to dense depth estimation from a single monocular camera that is moving through a dynamic scene. The approach produces a dense depth map from two consecutive frames. Moving objects are reconstructed along with the surrounding environment. We provide a novel motion segmentation algorithm that segments the optical flow field into a set of motion models, each with its own epipolar geometry. We then show that the scene can be reconstructed based on these motion models by optimizing a convex program. The optimization jointly reasons about the scales of different objects and assembles the scene in a common coordinate frame, determined up to a global scale. Experimental results demonstrate that the presented approach outperforms prior methods for monocular depth estimation in dynamic scenes.},
url = {http://approjects.co.za/?big=en-us/research/publication/dense-monocular-depth-estimation-in-complex-dynamic-scenes/},
}