Semantic decomposition and reconstruction of residential scenes from LiDAR data

Abstract

Figure 1: Our reconstruction pipeline. From left to right: (a) semantically labeled 3D point cloud; (b) reconstructed objects using categoryspecific methods, including billboard trees, replaced common objects, and a building. The color-code on the building shows recognized different building parts; (c) textured 3D models on a ground plane, and (d) an overview of an automatically reconstructed large-scale scene. AbstractWe present a complete system to semantically decompose and reconstruct 3D models from point clouds. Different than previous urban modeling approaches, our system is designed for residential scenes, which consist of mainly low-rise buildings that do not exhibit the regularity and repetitiveness as high-rise buildings in downtown areas. Our system first automatically labels the input into distinctive categories using supervised learning techniques. Based on the semantic labels, objects in different categories are reconstructed with domain-specific knowledge. In particular, we present a novel building modeling scheme that aims to decompose and fit the building point cloud into basic blocks that are blockwise symmetric and convex. This building representation and its reconstruction algorithm are flexible, efficient, and robust to missing data. We demonstrate the effectiveness of our system on various datasets and compare our building modeling scheme with other state-of-the-art reconstruction algorithms to show its advantage in terms of both quality and speed.

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