Kavli Affiliate: Feng Wang
| First 5 Authors: Liting Jiang, Feng Wang, Wenyi Zhang, Peifeng Li, Hongjian You
| Summary:
Stereo matching, a critical step of 3D reconstruction, has fully shifted
towards deep learning due to its strong feature representation of remote
sensing images. However, ground truth for stereo matching task relies on
expensive airborne LiDAR data, thus making it difficult to obtain enough
samples for supervised learning. To improve the generalization ability of
stereo matching networks on cross-domain data from different sensors and
scenarios, in this paper, we dedicate to study key training factors from three
perspectives. (1) For the selection of training dataset, it is important to
select data with similar regional target distribution as the test set instead
of utilizing data from the same sensor. (2) For model structure, cascaded
structure that flexibly adapts to different sizes of features is preferred. (3)
For training manner, unsupervised methods generalize better than supervised
methods, and we design an unsupervised early-stop strategy to help retain the
best model with pre-trained weights as the basis. Extensive experiments are
conducted to support the previous findings, on the basis of which we present an
unsupervised stereo matching network with good generalization performance. We
release the source code and the datasets at
https://github.com/Elenairene/RKF_RSSM to reproduce the results and encourage
future work.
| Search Query: ArXiv Query: search_query=au:”Feng Wang”&id_list=&start=0&max_results=3