Perceiving Unseen 3D Objects by Poking the Objects
This addresses the challenge of enabling robots to perceive and interact with unknown objects in unstructured environments, representing a novel interactive method rather than an incremental improvement.
The paper tackles the problem of 3D object perception for robots without relying on known models or annotated data by proposing a poking-based approach that automatically discovers and reconstructs unseen 3D objects, achieving high-quality results in real-world experiments.
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.