  The task of object viewpoint estimation has been a challenge since the early
days of computer vision. To estimate the viewpoint (or pose) of an object,
people have mostly looked at object intrinsic features, such as shape or
appearance. Surprisingly, informative features provided by other, extrinsic
elements in the scene, have so far mostly been ignored. At the same time,
contextual cues have been proven to be of great benefit for related tasks such
as object detection or action recognition. In this paper, we explore how
information from other objects in the scene can be exploited for viewpoint
estimation. In particular, we look at object configurations by following a
relational neighbor-based approach for reasoning about object relations. We
show that, starting from noisy object detections and viewpoint estimates,
exploiting the estimated viewpoint and location of other objects in the scene
can lead to improved object viewpoint predictions. Experiments on the KITTI
dataset demonstrate that object configurations can indeed be used as a
complementary cue to appearance-based viewpoint estimation. Our analysis
reveals that the proposed context-based method can improve object viewpoint
estimation by reducing specific types of viewpoint estimation errors commonly
made by methods that only consider local information. Moreover, considering
contextual information produces superior performance in scenes where a high
number of object instances occur. Finally, our results suggest that, following
a cautious relational neighbor formulation brings improvements over its
aggressive counterpart for the task of object viewpoint estimation.
