Abstract: Long-range laser scanners can capture dense point-clouds from indoor and outdoor scenes. In this keynote, we discuss point processing and shape reconstruction techniques for large-scale point-clouds. We first discuss shape reconstruction of manufacturing plants using terrestrial laser scanning. 3D models of engineering plants are useful for simulating reorganization of production lines. However, it is not easy to reconstruct object shapes from huge, noisy, and incomplete point-clouds. To solve these problems, we introduce efficient surface detection and template-based shape modeling techniques. Then we discuss point processing for mobile mapping systems (MMS). A MMS can capture point-clouds of road surfaces, buildings, and roadside objects while running on the road. Since roadside objects, such as utility poles, traffic signs, streetlights, guardrails, have to be maintained periodically, their 3D models are useful for planning maintenance tasks without on-site survey. In this keynote, we introduce our point processing techniques for segmentation, mesh generation, object classification, and shape reconstruction from MMS data.