With the rapid development of measurement technology, LiDAR and depth cameras
are widely used in the perception of the 3D environment. Recent learning based
methods for robot perception most focus on the image or video, but deep
learning methods for dynamic 3D point cloud sequences are underexplored.
Therefore, developing efficient and accurate perception method compatible with
these advanced instruments is pivotal to autonomous driving and service robots.
An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv)
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 the
spatio-temporal attention mechanism. Then, anchor-based 3D convolution is
adopted to aggregate these anchors' features to the core points. The proposed
method makes better use of the structured information within the local region
and learns spatio-temporal embedding features from dynamic 3D point cloud
sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural
Networks (ASTA3DCNNs) are built for classification and segmentation tasks based
on the proposed ASTA3DConv and evaluated on action recognition and semantic
segmentation tasks. The experiments and ablation studies on MSRAction3D and
Synthia datasets demonstrate the superior performance and effectiveness of our
method for dynamic 3D point cloud sequences. Our method achieves the
state-of-the-art performance among the methods with dynamic 3D point cloud
sequences as input on MSRAction3D and Synthia datasets.

本文提出了一种基于锚点的时空关注的 3D 卷积操作（ASTA3DConv）来处理动态 3D 点云序列，建立了 Anchor-based Spatio-Temporal Attention 3D 卷积神经网络 (ASTA3DCNNs) 进行分类和分割任务。在实验中，该方法在 MSRAction3D 和 Synthia 数据集上展现了出色的性能和有效性，并取得了动态 3D 点云序列作为输入的最新性能。