  Recently, learning based methods for the robot perception from the image or
video have much developed, but deep learning methods for dynamic 3D point cloud
sequences are underexplored. With the widespread application of 3D sensors such
as LiDAR and depth camera, efficient and accurate perception of the 3D
environment from 3D sequence data is pivotal to autonomous driving and service
robots. An Anchor-based Spatial-Temporal Attention Convolution operation
(ASTAConv) is proposed in this paper to process dynamic 3D point cloud
sequences. The proposed convolution operation builds a regular receptive field
around each point by setting several virtual anchors around each point. The
features of neighborhood points are firstly aggregated to each anchor based on
spatial-temporal attention mechanism. Then, anchor-based sparse 3D convolution
is adopted to aggregate the features of these anchors to the core points. The
proposed method makes better use of the structured information within the local
region, and learn spatial-temporal embedding features from dynamic 3D point
cloud sequences. Then Anchor-based Spatial-Temporal Attention Convolutional
Neural Networks (ASTACNNs) are proposed for classification and segmentation
tasks and are evaluated on action recognition and semantic segmentation tasks.
The experimental results on MSRAction3D and Synthia datasets demonstrate that
the higher accuracy can be achieved than the previous state-of-the-art method
by our novel strategy of multi-frame fusion.
