An Unsupervised Registration of 3d Point Clouds to 2d Cad Model: A Case Study of Floor Plan

Abstract

Abstract. Thanks to the proliferation of commodity 3D devices such as HoloLens, one can have easy access to the 3D model of indoor building objects. However, this model does not match 2D available computer-aided design (CAD) models as the as-built model. To address this problem, in this study, a 3-step registration method is proposed. First, binary images, including walls and background, are generated for the 3D point cloud (PC) and the 2D CAD model. Then, 2D-to-2D corresponding pixels (CPs) are extracted based on the intersection of walls in each binary image of PC (BIPC) and binary CAD (BCAD) model. Since the 3D PC space coordinates (XYZ) of all BIPC's pixels are known, BIPC part of the 2D-to-2D CPs can be considered 3D. Lastly, the parameters of the 8-parameter affine are estimated using the 2D-to-3D CPs, which are pixel coordinates in BCAD model as well as their correspondences in the 3D PC space. Experimental results indicate the efficiency of our proposed method compared to manual registration.

Gaussian processes for 3D measurements

“…The synthetically generated point clouds are based on objects drawn in the free and open source Blender 3.3 LTS 2 . Point clouds resulting from real world scans were created on an Android mobile phone using the photogrammetry KIRI engine 3 and imported in Blender, where they are cleaned up by dissolving disconnected points, removing the background and subsampling using standard Blender tools. However, they still contain some overlapping triangles and other mesh irregularities.…”

“…Extracting structural information as shapes or surfaces from an unordered set of 3D coordinates (point cloud) has been an important topic in computer vision [64]. It is a crucial part of many applications such as autonomous driving [24], scene understanding [11], reverse engineering of geometric models [156], quality control [4], simultaneous localisation and mapping (SLAM) [146] and matching point clouds to CAD models [3]. Over the last decade, hardware developments have made the acquisition of those point clouds more affordable.…”