A one-shot next best view system for active object recognition

摘要

Active vision is the ability of intelligent agents to dynamically gather more information about their surroundings by physical motion of the camera. In the case of object recognition, active vision enables improved performance by incorporating classification decisions from new viewpoints when there is some degree of uncertainty in the current recognition result. A natural question in an autonomous active vision system is, nonetheless, how to determine the new viewpoint, i.e. in what pose should the camera be moved? This is the traditional question of next best view in active perception systems. Current approaches to the next best view problem either need construction of occupancy grids or require training datasets of 3D objects or multiple captures of the same object in specified poses. Occupancy grid methods are usually dependent on multiple camera movements to perform well, which make them more useful for 3D reconstruction applications than object recognition. In this paper, a next best view method for active object recognition based on object appearance and surface direction is proposed that decides on the next cameras pose without requiring any specifically structured training datasets of 3D objects. It is also designed for single-shot deductions of next viewpoint and is able to determine next best views without the need for substantial knowledge of 3D voxels in the environment around the camera. The experimental results illustrate the efficiency of the proposed method, while showing large improvements in accuracy and F1 score.