Point Cloud Semantic Segmentation

TL;DR

This paper evaluates various deep learning models for semantic segmentation of 3D point clouds, specifically on the S3DIS dataset, comparing their accuracy and inference speed.

Contribution

It provides a comprehensive comparison of multiple state-of-the-art point cloud segmentation models on a standard dataset, highlighting their performance differences.

Findings

PointCNN achieves high accuracy on S3DIS.

Point Transformer offers a good balance of speed and accuracy.

Cylinder3D demonstrates strong segmentation performance.

Abstract

Semantic segmentation is an important and well-known task in the field of computer vision, in which we attempt to assign a corresponding semantic class to each input element. When it comes to semantic segmentation of 2D images, the input elements are pixels. On the other hand, the input can also be a point cloud, where one input element represents one point in the input point cloud. By the term point cloud, we refer to a set of points defined by spatial coordinates with respect to some reference coordinate system. In addition to the position of points in space, other features can also be defined for each point, such as RGB components. In this paper, we conduct semantic segmentation on the S3DIS dataset, where each point cloud represents one room. We train models on the S3DIS dataset, namely PointCNN, PointNet++, Cylinder3D, Point Transformer, and RepSurf. We compare the obtained results with respect to standard evaluation metrics for semantic segmentation and present a comparison of the models based on inference speed.