PCAN: 3D Attention Map Learning Using Contextual Information for Point
Cloud Based Retrieval
Abstract
Point cloud based retrieval for place recognition is an
emerging problem in vision field. The main challenge is how
to find an efficient way to encode the local features into a
discriminative global descriptor. In this paper, we propose
a Point Contextual Attention Network (PCAN), which can
predict the significance of each local point feature based on
point context. Our network makes it possible to pay more attention to the task-relevent features when aggregating local
features. Experiments on various benchmark datasets show
that the proposed network can provide outperformance than
current state-of-the-art approaches.