论文标题
Silhonet-Fisheye：基于ROI的对象姿势估计网络的改编对单眼鱼眼图像
SilhoNet-Fisheye: Adaptation of A ROI Based Object Pose Estimation Network to Monocular Fisheye Images
论文作者
论文摘要
对于基于单眼图像的对象姿势估计，对深度学习方法的最新兴趣。虽然对象姿势估计是与物理世界的自主机器人相互作用的重要问题，而基于单眼方法的应用空间是广泛的，但在将这些方法应用于Fisheye Imaging Systems上几乎没有工作。同样，很少有带注释的鱼眼图像数据集可以开发和测试这些方法。对于在水下域，基于鱼眼图像或其他方式应用的对象检测方法中，研究领域更加稀疏。在这项工作中，我们提出了一个新颖的框架，用于调整基于ROI的6D对象姿势估计方法来处理完整的Fisheye图像。该方法结合了中间球形图像表示感兴趣区域的GNOMIC投影，以纠正鱼眼变形。此外，我们为在自然的水下环境中收集的称为Uwhandles的鱼眼图像数据集，带有6D对象姿势和2D边界盒注释。
There has been much recent interest in deep learning methods for monocular image based object pose estimation. While object pose estimation is an important problem for autonomous robot interaction with the physical world, and the application space for monocular-based methods is expansive, there has been little work on applying these methods with fisheye imaging systems. Also, little exists in the way of annotated fisheye image datasets on which these methods can be developed and tested. The research landscape is even more sparse for object detection methods applied in the underwater domain, fisheye image based or otherwise. In this work, we present a novel framework for adapting a ROI-based 6D object pose estimation method to work on full fisheye images. The method incorporates the gnomic projection of regions of interest from an intermediate spherical image representation to correct for the fisheye distortions. Further, we contribute a fisheye image dataset, called UWHandles, collected in natural underwater environments, with 6D object pose and 2D bounding box annotations.
