PVStereo：ピラミッド投票モジュールによる自己教師ありステレオマッチング
PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching
ピラミッド投票モジュールで信頼できる準密な視差画像を生成し、それを教師信号として再帰型ネットワークOptStereoを自己教師あり学習させるステレオマッチング手法を提案した論文。
著者: Hengli Wang, Rui Fan, Peide Cai, Ming Liu
分類: cs.CV, cs.RO
原文アブストラクト
Supervised learning with deep convolutional neural networks (DCNNs) has seen huge adoption in stereo matching. However, the acquisition of large-scale datasets with well-labeled ground truth is cumbersome and labor-intensive, making supervised learning-based approaches often hard to implement in practice. To overcome this drawback, we propose a robust and effective self-supervised stereo matching approach, consisting of a pyramid voting module (PVM) and a novel DCNN architecture, referred to as OptStereo. Specifically, our OptStereo first builds multi-scale cost volumes, and then adopts a recurrent unit to iteratively update disparity estimations at high resolution; while our PVM can generate reliable semi-dense disparity images, which can be employed to supervise OptStereo training. Furthermore, we publish the HKUST-Drive dataset, a large-scale synthetic stereo dataset, collected under different illumination and weather conditions for research purposes. Extensive experimental results demonstrate the effectiveness and efficiency of our self-supervised stereo matching approach on the KITTI Stereo benchmarks and our HKUST-Drive dataset. PVStereo, our best-performing implementation, greatly outperforms all other state-of-the-art self-supervised stereo matching approaches. Our project page is available at sites.google.com/view/pvstereo.
