Richer Aggregated Features for Optical Flow Estimation with Edge-aware Refinement
Xianshun Wang, Dongchen Zhu, Jiafei Song, Yanqing Liu, Jiamao Li, Xiaolin Zhang
Abstract
Recent CNN-based optical flow approaches have a separated structure of feature extraction and flow estimation. The core task of optical flow is finding the corresponding points while rich representation is just the key part of such matching problems. However, the prior work usually pays more attention to the design of flow decoder than the feature extraction. In this paper, we present a novel optical flow estimation network to enrich the feature representation of each pyramid level, with a hierarchical dilated architecture and a bottom-up aggregation scheme. In addition, inspired by edge guided classical methods, we bring the edge-aware idea into our approach and propose an edge-aware refinement (EAR) subnetwork to handle motion boundaries. Using the same decoding structure as PWC-Net, our network outperforms it by a large margin and leads all its derivatives both on KITTI-2012 and KITTI-2015. Further performance analysis proves the effectiveness of proposed ideas.
BibTeX
@inproceedings{iros2020_richeraggregated,
title = {Richer Aggregated Features for Optical Flow Estimation with Edge-aware Refinement},
author = {Xianshun Wang and Dongchen Zhu and Jiafei Song and Yanqing Liu and Jiamao Li and Xiaolin Zhang},
booktitle = {IROS 2020},
year = {2020}
}