  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 https://github.com/tpark94/spnv2.
