Volumetric Representation and Sphere Packing of Indoor Space for Three-Dimensional Room Segmentation

Fan Yang,Chi Zhang,Xinkai Zuo,Jiyi Zhang,Lin Li,Mingliang Che

doi:10.3390/ijgi10110739

Abstract

Room segmentation is a basic task for the semantic enrichment of point clouds. Recent studies have mainly projected single-floor point clouds to binary images to realize two-dimensional room segmentation. However, these methods have difficulty solving semantic segmentation problems in complex 3D indoor environments, including cross-floor spaces and rooms inside rooms; this is the bottleneck of indoor 3D modeling for non-Manhattan worlds. To make full use of the abundant geometric and spatial structure information in 3D space, a novel 3D room segmentation method that realizes room segmentation directly in 3D space is proposed in this study. The method utilizes volumetric representation based on a VDB data structure and packs an indoor space with a set of compact spheres to form rooms as separated connected components. Experimental results on different types of indoor point cloud datasets demonstrate the efficiency of the proposed method.

Highlights

Geo-Inf. 2021, 10, 739. https://The continuous progress of laser scanning technology provides an effective means for the measurement and perception of architectural information
To validate the feasibility of the proposed method, many different types of indoor point cloud data are selected for experiments
The datasets were acquired with a camera and a depth sensor mounted on a motorized tripod

Summary

Introduction

Geo-Inf. 2021, 10, 739. https://The continuous progress of laser scanning technology provides an effective means for the measurement and perception of architectural information. Laser point clouds are usually unstructured and lack semantic information, while automatic. The purpose of the room segmentation task is to automatically and robustly partition indoor 3D point clouds into rooms. The current methods mainly project single-floor point clouds to two-dimensional occupancy probability images to realize twodimensional room segmentation [12,13]. These methods have difficulty solving semantic segmentation problems in complex 3D indoor environments, including cross-floor spaces [14] and rooms inside rooms [15]; this is the bottleneck of indoor 3D modeling for non-Manhattan worlds.

Methods

Results

Discussion

Conclusion