论文标题
RESFPN：多分辨率的残留跳过连接功能金字塔网络，以进行准确的密集像素匹配
ResFPN: Residual Skip Connections in Multi-Resolution Feature Pyramid Networks for Accurate Dense Pixel Matching
论文作者
论文摘要
许多计算机视觉算法（例如差异，光流或场景流量估计）需要密集的像素匹配。特征金字塔网络（FPN）已被证明是基于CNN的密集匹配任务的合适特征提取器。 FPN在多个尺度上生成局部良好的局部和语义上强特征。但是，由于其合理但有限的本地化精度，通用FPN并未利用其全部潜力。因此，我们提出ResFPN-一个具有多个残留跳过连接的多分辨率特征金字塔网络，在任何规模上，我们从更高分辨率地图中利用信息来获得更强和更好的局部特征。在我们的消融研究中，我们证明了与FPN相比，精度明显更高的新型建筑的有效性。此外，我们在许多不同的像素匹配应用程序（例如Kitti，Sintel和FlyingThings3D）上验证了RESFPN的卓越精度。
Dense pixel matching is required for many computer vision algorithms such as disparity, optical flow or scene flow estimation. Feature Pyramid Networks (FPN) have proven to be a suitable feature extractor for CNN-based dense matching tasks. FPN generates well localized and semantically strong features at multiple scales. However, the generic FPN is not utilizing its full potential, due to its reasonable but limited localization accuracy. Thus, we present ResFPN -- a multi-resolution feature pyramid network with multiple residual skip connections, where at any scale, we leverage the information from higher resolution maps for stronger and better localized features. In our ablation study, we demonstrate the effectiveness of our novel architecture with clearly higher accuracy than FPN. In addition, we verify the superior accuracy of ResFPN in many different pixel matching applications on established datasets like KITTI, Sintel, and FlyingThings3D.
