DSACNN: Dynamically local self-attention CNN for 3D point cloud analysis

The point cloud is a common 3D representation widely applied in CAX engineering due to its simple data representation and rich semantic information. However, discrete and unordered 3D data structures make it difficult for point clouds to understand semantic information and make them unsuitable for applying standard operators. In this paper, to enhance machine perception of 3D semantic information, we propose a novel approach that can not only directly process point cloud data by a novel convolution-like operator but also dynamically pay attention to local semantic information. First, we design a novel dynamic local self-attention mechanism that can dynamically and flexibly focus on top-level information of the receptive field to learn and understand subtle features. Second, we propose a dynamic self-attention learning block, which adopts the proposed dynamic local self-attention learning convolution operation to directly deal with disordered and irregular point clouds to learn global and local point features while dynamically learning the important local semantic information. Third, the proposed operation can be compatibly applied as an independent component in popular architectures to improve the perception of local semantic information. Numerous experiments demonstrate the advantage of our method for point cloud tasks on datasets from both CAD data and scan data of complex real-world scenes.

Automated summary

In this paper, a novel approach is proposed to enhance the machine perception of 3D semantic information in point clouds. The approach includes a dynamic local self-attention mechanism and a dynamic self-attention learning block, which can handle unordered and irregular point cloud data, learn global and local features, and dynamically learn important local semantic information. The method shows advantages in point cloud tasks.