Back-Projective Priming: Toward Efficient 3d Model-based Object Recognition via Preemptive Top-down Constraints

摘要

This paper describes a novel framework for context-based object-recognition/pose-estimation. High-level geometric constraints are used to optimize fitting of a 3d model to a 2d image through a process termed "back-projective priming". A practical problem in robotics, electrical outlet discovery, is used for testing. The robot, experimental setup, and ongoing/future work are described.