  Yaw estimation of boats from the viewpoint of unmanned aerial vehicles (UAVs)
and unmanned surface vehicles (USVs) or boats is a crucial task in various
applications such as 3D scene rendering, trajectory prediction, and navigation.
However, the lack of literature on yaw estimation of objects from the viewpoint
of UAVs has motivated us to address this domain. In this paper, we propose a
method based on HyperPosePDF for predicting the orientation of boats in the 6D
space. For that, we use existing datasets, such as PASCAL3D+ and our own
datasets, SeaDronesSee-3D and BOArienT, which we annotated manually. We extend
HyperPosePDF to work in video-based scenarios, such that it yields robust
orientation predictions across time. Naively applying HyperPosePDF on video
data yields single-point predictions, resulting in far-off predictions and
often incorrect symmetric orientations due to unseen or visually different
data. To alleviate this issue, we propose aggregating the probability
distributions of pose predictions, resulting in significantly improved
performance, as shown in our experimental evaluation. Our proposed method could
significantly benefit downstream tasks in marine robotics.
