A Framework for Building Point Cloud Cleaning, Plane Detection and Semantic Segmentation

TL;DR

This paper introduces a comprehensive framework combining point cloud cleaning, plane detection, and deep learning-based semantic segmentation to improve building modeling accuracy and efficiency.

Contribution

It presents an integrated approach that combines adaptive outlier removal, robust plane detection, and a PointNet-inspired deep learning model for semantic segmentation of building components.

Findings

Effective outlier removal using adaptive z-score thresholding.

Accurate plane detection with RANSAC for architectural elements.

Deep learning model achieves high accuracy in semantic segmentation.

Abstract

This paper presents a framework to address the challenges involved in building point cloud cleaning, plane detection, and semantic segmentation, with the ultimate goal of enhancing building modeling. We focus in the cleaning stage on removing outliers from the acquired point cloud data by employing an adaptive threshold technique based on z-score measure. Following the cleaning process, we perform plane detection using the robust RANSAC paradigm. The goal is to carry out multiple plane segmentations, and to classify segments into distinct categories, such as floors, ceilings, and walls. The resulting segments can generate accurate and detailed point clouds representing the building's architectural elements. Moreover, we address the problem of semantic segmentation, which plays a vital role in the identification and classification of different components within the building, such as walls, windows, doors, roofs, and objects. Inspired by the PointNet architecture, we propose a deep learning architecture for efficient semantic segmentation in buildings. The results demonstrate the effectiveness of the proposed framework in handling building modeling tasks, paving the way for improved accuracy and efficiency in the field of building modelization.