  In recent years there has been significant progress in the field of 3D
learning on classification, detection and segmentation problems. The vast
majority of the existing studies focus on canonical closed-set conditions,
neglecting the intrinsic open nature of the real-world. This limits the
abilities of robots and autonomous systems involved in safety-critical
applications that require managing novel and unknown signals. In this context
exploiting 3D data can be a valuable asset since it provides rich information
about the geometry of perceived objects and scenes. With this paper we provide
the first broad study on 3D Open Set learning. We introduce 3DOS: a novel
testbed for semantic novelty detection that considers several settings with
increasing difficulties in terms of semantic (category) shift, and covers both
in-domain (synthetic-to-synthetic, real-to-real) and cross-domain
(synthetic-to-real) scenarios. Moreover, we investigate the related 2D Open Set
literature to understand if and how its recent improvements are effective on 3D
data. Our extensive benchmark positions several algorithms in the same coherent
picture, revealing their strengths and limitations. The results of our analysis
may serve as a reliable foothold for future tailored 3D Open Set methods.
