  We present a novel approach to interactive 3D object perception for robots.
Unlike previous perception algorithms that rely on known object models or a
large amount of annotated training data, we propose a poking-based approach
that automatically discovers and reconstructs 3D objects. The poking process
not only enables the robot to discover unseen 3D objects but also produces
multi-view observations for 3D reconstruction of the objects. The reconstructed
objects are then memorized by neural networks with regular supervised learning
and can be recognized in new test images. The experiments on real-world data
show that our approach could unsupervisedly discover and reconstruct unseen 3D
objects with high quality, and facilitate real-world applications such as
robotic grasping. The code and supplementary materials are available at the
project page: https://zju3dv.github.io/poking_perception.
