Robust 3D Object Classification by Combining Point Pair Features and Graph Convolution

Abstract

Object classification is an important capability for robots as it provides vital semantic information that underpin most practical high-level tasks. Classic handcrafted features, such as point pair features, have demonstrated their robustness for this task. Combining these features with modern deep learning methods provide discriminative features that are rotation invariant and robust to various sources of noise. In this work, we aim to improve the descriptiveness of point pair features while retaining their robustness. We propose a method to achieve more structured sampling of pairs and combine this information through the use of graph convolutional networks. We introduce a novel attention model based on a repeatable local reference frame. Experiments show that our approach significantly improves the state of the art for object classification on large scale reconstruction such as the Stanford 3D indoor dataset and ScanNet and obtains competitive accuracy on the artificial dataset ModelNet.