Large-scale point cloud semantic segmentation method and system

Abstract

The invention relates to a large-scale point cloud semantic segmentation method and system, and the method comprises the steps: extracting point-by-point features of a to-be-recognized point cloud,wherein the to-be-recognized point cloud is composed of a plurality of to-be-recognized points; on the basis of the point cloud space information of each to-be-recognized point, gradually encoding each point-by-point feature to obtain a corresponding point cloud feature; decoding the point cloud features step by step to acquire corresponding decoding feature; and according to each decoding feature, based on a semantic segmentation network model, determining a semantic segmentation prediction result of the to-be-recognized 3D point cloud. The invention extracts point-by-point features of the to-be-recognized point cloud, extracting more effective spatial features from large-scale point cloud information, gradually encodes each point-by-point feature based on point cloud spatial information of each to-be-recognized point to obtain point cloud features, further decodes to obtain decoding features, and determining a semantic segmentation prediction result of the to-be-recognized 3D point cloud according to the decoding features. Therefore, the semantic information of the surrounding space environment is obtained, and semantic segmentation precision is improved.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a large-scale point cloud semantic segmentation method and system based on spatial context feature learning. Background technique [0002] In the mobile robot's surrounding environment perception system, the semantic segmentation of the surrounding environment is an important part, which provides the semantic understanding information of the environment for the mobile robot's decision-making control system. Compared with 2D image sensors, 3D sensors (such as lidar) can provide richer spatial geometric structure information, which is more helpful for mobile robots to understand their three-dimensional space. Therefore, with the rapid development of 3D sensors, the semantic segmentation of 3D point clouds has attracted much attention from academia and industry in recent years. Concerned about computer vision problems. [0003] Due to the unstructured and disordered nature...

AI Technical Summary

Problems solved by technology

This type of method has discretization errors, and the final semantic segmentation accuracy and understanding of the environment are affected by the degree of discretization

At the same time, the above two types of methods require additional complex point cloud spatial processing steps, such as projection and discretization, and their high computational complexity makes it impossible to handle large-scale point clouds.

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