Three-dimensional semantic segmentation method based on channel attention and multi-scale fusion

Abstract

The invention belongs to the technical field of three-dimensional point cloud data processing, and discloses a three-dimensional point cloud semantic segmentation method based on channel attention and multi-scale fusion. Firstly, to-be-segmented point cloud data is read, preprocessed and then input into a segmentation network; and then sequentially passing through four modules consisting of an encoder and a channel attention layer, wherein the encoder comprises a down-sampling layer, a grouping layer and a position self-adaptive convolution. Then, a multi-scale convolution context module is used for extracting point cloud context information, and finally, the point cloud context information sequentially passes through four decoders composed of upper sampling layers and unit Point Net networks. And the final segmentation result is obtained through a full connection layer with the size of k (category number). According to the method, the position information of the point cloud is fully utilized, the channel attention layer is introduced to re-calibrate the point cloud features in the channel dimension, more attention is paid to channel information useful for the segmentation task, a multi-scale convolution context module is further provided, and the segmentation task is more accurate. The features of different scales are captured in parallel by adopting cavity convolution with the same expansion rate and different kernel sizes, so that the segmentation result is improved.

Description

technical field [0001] The invention belongs to the technical field of three-dimensional point cloud data processing, and in particular relates to a three-dimensional point cloud semantic segmentation method based on channel attention and multi-scale fusion. Background technique [0002] With the development and rise of artificial intelligence technology, 3D point cloud data analysis has attracted widespread attention. Compared with two-dimensional images, 3D point clouds contain richer three-dimensional spatial information and are not affected by external factors such as illumination and perspective. More accurate and comprehensive characterization of the model. As the key content of scene understanding, 3D point cloud segmentation is one of the frontier research directions of artificial intelligence, and it is widely used in the fields of robotics, virtual reality, autonomous driving, and laser remote sensing measurement. [0003] Point cloud segmentation methods can be d...

AI Technical Summary

Problems solved by technology

Due to the occlusion problem of objects in real scenes, after projecting objects onto a two-dimensional plane, part of the information will be lost, and the spatial structure information contained in the three-dimensional data cannot be fully utilized. The selection of the projection plane will also have a certain impact on the results of the algorithm.

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