A method for semantic segmentation of scene point cloud

Abstract

The invention belongs to the technical field of computer vision. A method for semantic segmentation of scene point cloud is provided, a framework of point cloud semantic segmentation model for large-scale dense scene based on depth learning technology is designed, For the input large-scale dense scene point cloud, it can transform the three-dimensional information of point cloud into two-dimensional information which can be processed directly by convolution without losing the information, and complete the task of point cloud semantic segmentation combined with image semantic segmentation technology. In this framework, we can effectively solve the semantic segmentation task of large-scale dense scene point cloud. The semantic segmentation result of the scene point cloud obtained by the method of the invention can be directly used in tasks such as robot navigation, automatic driving and the like. And this method is especially effective in non-synthetic natural scenes.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a method for semantically segmenting large-scale dense point cloud scenes based on deep learning. Background technique [0002] The use of convolutional neural networks to process 2D images dominates the development of modern computer vision. A key factor for its success is the efficient processing of convolutions on images. Convolutions are defined on a regular grid in the image, which enables extremely efficient implementation of convolution operations. This property enables the use of powerful deep architectures for processing large datasets with high resolution. [0003] When analyzing large-scale 3D scenes, a straightforward extension of the above approach is to perform 3D convolutions on a voxel grid. However, this voxel-based approach has significant limitations, including issues such as cubic growth of memory consumption and computational efficiency...

AI Technical Summary

Problems solved by technology

[0013] In order to solve the technical problems such as the traditional point cloud scene understanding is easily limited by data resolution, local features are not robust enough, and it is difficult to handle large-scale dense point clouds, a large-scale dense scene point cloud semantic segmentation model based on deep learning technology is designed. The framework, for the input of large-scale dense scene point cloud, can convert the three-dimensional information of the point cloud into two-dimensional information that can be directly processed by convolution without losing information, and combine the technology of image semantic segmentation to complete the point cloud The task of semantic segmentation

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