BibTex format
@unpublished{Xu:2020,
author = {Xu, B and Davison, AJ and Leutenegger, S},
publisher = {arXiv},
title = {Deep probabilistic feature-metric tracking},
url = {http://arxiv.org/abs/2008.13504v1},
year = {2020}
}
Publications
Citation
RIS format (EndNote, RefMan)
TY - UNPB
AB - Dense image alignment from RGB-D images remains a critical issue forreal-world applications, especially under challenging lighting conditions andin a wide baseline setting. In this paper, we propose a new framework to learna pixel-wise deep feature map and a deep feature-metric uncertainty mappredicted by a Convolutional Neural Network (CNN), which together formulate adeep probabilistic feature-metric residual of the two-view constraint that canbe minimised using Gauss-Newton in a coarse-to-fine optimisation framework.Furthermore, our network predicts a deep initial pose for faster and morereliable convergence. The optimisation steps are differentiable and unrolled totrain in an end-to-end fashion. Due to its probabilistic essence, our approachcan easily couple with other residuals, where we show a combination with ICP.Experimental results demonstrate state-of-the-art performance on the TUM RGB-Ddataset and 3D rigid object tracking dataset. We further demonstrate ourmethod's robustness and convergence qualitatively.
AU - Xu,B
AU - Davison,AJ
AU - Leutenegger,S
PB - arXiv
PY - 2020///
TI - Deep probabilistic feature-metric tracking
UR - http://arxiv.org/abs/2008.13504v1
UR - http://hdl.handle.net/10044/1/82605
ER -