# Point Cloud Semantic Segmentation

Source: [Point Cloud Semantic Segmentation](<https://www.sota2.com/research/paper/68fa6f361e209131cea44790>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:16:36.929Z

Authors: Ivan Martinovi\\'c

Published: 2023-05-01T11:20:51.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2305.00773>)

[PDF](<https://arxiv.org/pdf/2305.00773.pdf>)