Title

Point2node: Correlation learning of dynamic-node for point cloud feature modeling

Document Type

Conference Proceeding

Publication Date

1-1-2020

Abstract

Fully exploring correlation among points in point clouds is essential for their feature modeling. This paper presents a novel end-to-end graph model, named Point2Node, to represent a given point cloud. Point2Node can dynamically explore correlation among all graph nodes from different levels, and adaptively aggregate the learned features. Specifically, first, to fully explore the spatial correlation among points for enhanced feature description, in a high-dimensional node graph, we dynamically integrate the node's correlation with self, local, and non-local nodes. Second, to more effectively integrate learned features, we design a data-aware gate mechanism to self-adaptively aggregate features at the channel level. Extensive experiments on various point cloud benchmarks demonstrate that our method outperforms the state-of the- art.

Publication Source (Journal or Book title)

AAAI 2020 - 34th AAAI Conference on Artificial Intelligence

First Page

10925

Last Page

10932

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