3D Point Cloud Semantic Segmentation System Based on Lightweight FPConv

Abstract

In this paper, we proposed a 3D point cloud semantic segmentation system based on lightweight FPConv. In 3D point cloud mapping, data is depicted in a 3D space to represent 3D imagery data. These maps are collected through direct measurements; all points in a 3D point cloud map corresponds to a measurement point and, therefore, contains a large amount of data. Data in 3D point cloud maps are stored in point clouds, and they are extracted using 3D image processing or deep learning. However, because of the non-structured and high-dimensional properties of point clouds, the development of 3-D image recognition applications in the field of computer vision warrants further exploration. Large-scale neural networks are highly accurate, but they have the disadvantages of high computation complexity and low portability. Therefore, the present study proposed a 3D point cloud semantic segmentation system based on lightweight FPConv. The proposed network combines depth-wise separate convolution, quantization, and Winograd convolution technology to lighten and accelerate neural network computation. The performance of the presented network was verified using the Stanford 3D Large-Scale Indoor Spaces (S3DIS) large scene database provided by Stanford 3D AI Lab. The results reveals the excellent performance of the proposed model.