This work presents Spacecraft Pose Network v2 (SPNv2), a Convolutional Neural Network (CNN) for pose estimation of noncooperative spacecraft across domain gap. SPNv2 is a multi-scale, multi-task CNN which consists of a shared multi-scale feature encoder and multiple prediction heads that perform different tasks on a shared feature output. These tasks are all related to detection and pose estimation of a target spacecraft from an image, such as prediction of pre-defined satellite keypoints, direct pose regression, and binary segmentation of the satellite foreground. It is shown that by jointly training on different yet related tasks with extensive data augmentations on synthetic images only, the shared encoder learns features that are common across image domains that have fundamentally different visual characteristics compared to synthetic images. This work also introduces Online Domain Refinement (ODR) which refines the parameters of the normalization layers of SPNv2 on the target domain images online at deployment. Specifically, ODR performs self-supervised entropy minimization of the predicted satellite foreground, thereby improving the CNN's performance on the target domain images without their pose labels and with minimal computational efforts. The GitHub repository for SPNv2 is available at \url{https://github.com/tpark94/spnv2}.
翻译:这项工作展示了Spacecraft Pose 网络 v2( SPNv2), 一个用于对跨域空隙的不合作航天器进行估计的进化神经网络(CNN) 。 Spnv2 是一个多尺度、多任务CNN, 包括一个共享的多尺度特征编码器和多个预测头, 在一个共享的特性输出上执行不同任务。 这些任务都与探测有关, 从图像中对目标航天器进行估计, 如预测预先定义的卫星关键点、 直接成形回归和卫星地表层的二进化分解。 事实证明, 共享的编码器通过仅对合成图像进行广泛数据增强的不同相关任务进行联合培训, 来学习与合成图像相比具有根本不同视觉特性的图像领域共同的特征。 这项工作还引入了在线 Domain Refination( ODR), 改进了目标域图像在线部署时SPNV2 的正常化层参数。 具体来说, 网上对预测的地面卫星进行自我监督的最小化的加密最小化的加密最小化, 从而改进了CNN在目标域图库2 PROp2 的Smarp2 的图像的运行的运行中, 是其最低的标签和最小化。