Abstract: For point cloud recognition, methods of mapping point cloud data into two-dimensional pictures or restoring it to three-dimensional space have some shortcomings, such as large computation complexity and poor universality of scene. To address the problems, this paper proposes a method of deep residual learning network based on attention mechanism. The method obtains the weight distribution and key points of different points in the point cloud by the attention mechanism, and directly uses the point cloud data for efficient recognition. By the experiment, this paper studies and compares the recognition ability of different methods on the datasets MNIST and ModelNet40. The experimental results show that, compared with the methods respectively based on two-dimensional pictures, based on three-dimensional space and point cloud processing directly, the proposed method has the advantages of small parameter, small calculation and higher efficiency while ensuring high recognition accuracy.

Key words: point cloud recognition, attention mechanism, residual learning, small parameter； efficient