Point Pair Feature based Object Detection for Random Bin Picking

TL;DR

This paper evaluates point pair features for 3D object detection in industrial bin picking, introduces a synthetic dataset generation method, and proposes a heuristic to enhance robustness, speed, and accuracy.

Contribution

It presents a new synthetic data generation approach and a heuristic method to improve point pair feature-based detection in cluttered environments.

Findings

Enhanced robustness, speed, and accuracy over naive methods

Effective synthetic dataset generation for cluttered scenes

Analysis of existing solutions' strengths and weaknesses

Abstract

Point pair features are a popular representation for free form 3D object detection and pose estimation. In this paper, their performance in an industrial random bin picking context is investigated. A new method to generate representative synthetic datasets is proposed. This allows to investigate the influence of a high degree of clutter and the presence of self similar features, which are typical to our application. We provide an overview of solutions proposed in literature and discuss their strengths and weaknesses. A simple heuristic method to drastically reduce the computational complexity is introduced, which results in improved robustness, speed and accuracy compared to the naive approach.