  Object encoding and identification are vital for robotic tasks such as
autonomous exploration, semantic scene understanding, and re-localization.
Previous approaches have attempted to either track objects or generate
descriptors for object identification. However, such systems are limited to a
"fixed" partial object representation from a single viewpoint. In a robot
exploration setup, there is a requirement for a temporally "evolving" global
object representation built as the robot observes the object from multiple
viewpoints. Furthermore, given the vast distribution of unknown novel objects
in the real world, the object identification process must be class-agnostic. In
this context, we propose a novel temporal 3D object encoding approach, dubbed
AirObject, to obtain global keypoint graph-based embeddings of objects.
Specifically, the global 3D object embeddings are generated using a temporal
convolutional network across structural information of multiple frames obtained
from a graph attention-based encoding method. We demonstrate that AirObject
achieves the state-of-the-art performance for video object identification and
is robust to severe occlusion, perceptual aliasing, viewpoint shift,
deformation, and scale transform, outperforming the state-of-the-art
single-frame and sequential descriptors. To the best of our knowledge,
AirObject is one of the first temporal object encoding methods. Source code is
available at https://github.com/Nik-V9/AirObject.
