Structure-preserving Planar Simplification for Indoor Environments

TL;DR

This paper introduces a structure-preserving planar simplification method for indoor scene point clouds, combining segmentation, primitive plane extraction, mesh generation, and surface reconstruction to improve indoor environment modeling.

Contribution

It presents a novel, comprehensive approach for simplifying indoor scene point clouds while preserving structural details, including multi-story and slanted walls, with robust segmentation and mesh techniques.

Findings

Effective segmentation of structured and non-structured scene components

High fidelity in mesh generation and surface reconstruction

Quantitative improvements over existing surface reconstruction methods

Abstract

This paper presents a novel approach for structure-preserving planar simplification of indoor scene point clouds for both simulated and real-world environments. Initially, the scene point cloud undergoes preprocessing steps, including noise reduction and Manhattan world alignment, to ensure robustness and coherence in subsequent analyses. We segment each captured scene into structured (walls-ceiling-floor) and non-structured (indoor objects) scenes. Leveraging a RANSAC algorithm, we extract primitive planes from the input point cloud, facilitating the segmentation and simplification of the structured scene. The best-fitting wall meshes are then generated from the primitives, followed by adjacent mesh merging with the vertex-translation algorithm which preserves the mesh layout. To accurately represent ceilings and floors, we employ the mesh clipping algorithm which clips the ceiling and floor meshes with respect to wall normals. In the case of indoor scenes, we apply a surface reconstruction technique to enhance the fidelity. This paper focuses on the intricate steps of the proposed scene simplification methodology, addressing complex scenarios such as multi-story and slanted walls and ceilings. We also conduct qualitative and quantitative performance comparisons against popular surface reconstruction, shape approximation, and floorplan generation approaches.