Abstract
Abstract. This paper deals with 3D modeling of building interiors from point clouds captured by a 3D LiDAR scanner. Indeed, currently, the building reconstruction processes remain mostly manual. While LiDAR data have some specific properties which make the reconstruction challenging (anisotropy, noise, clutters, etc.), the automatic methods of the state-of-the-art rely on numerous construction hypotheses which yield 3D models relatively far from initial data. The choice has been done to propose a new modeling method closer to point cloud data, reconstructing only scanned areas of each scene and excluding occluded regions. According to this objective, our approach reconstructs LiDAR scans individually using connected polygons. This modeling relies on a joint processing of an image created from the 2D LiDAR angular sampling and the 3D point cloud associated to one scan. Results are evaluated on synthetic and real data to demonstrate the efficiency as well as the technical strength of the proposed method.
Highlights
Since the 1990’s, architects and civil engineering professionals use digital models to perform simulations and accurate computations before construct a building
We validated visually our algorithm on various LiDAR scans, and we present the results for two challenging ones
Building modeling is commonly divided into two stages, the segmentation of the point cloud and the fitting of models on these points
Summary
Since the 1990’s, architects and civil engineering professionals use digital models to perform simulations and accurate computations before construct a building. The new challenge is to obtain as-built models from existing buildings, which would notably be useful in case of renovations. For this purpose, the LiDAR technology is mostly used to scan building interiors because of its accuracy and its efficiency. The major scientific lock to the modeling of this structure lies in the accurate location of the planes, the polygonal contours and the polygons’ connections. The difficulty of this task comes mostly from the acquisition faults of a LiDAR device i.e., sampling anisotropy, measurement noise, occlusion problems, etc
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