Point Pair Features Based Object Recognition with Improved Training Pipeline

PPF (point pair feature) is a widely used framework in object detection and pose estimation. However, it is computational expensive and sensitive to cluster and occlusions. In this paper, we propose a new training pipeline for PPF which makes use of the visibility information of point pairs, yet with no extra computation cost. We also design a strategy to employ plane features to make PPF more discriminative and efficient. Our experiment results show that our method achieves competitive results compared with some state-of-the-art methods.

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