Reconstructing geometric shapes from point clouds is a common task that is often accomplished by experts manually modeling geometries in CAD-capable software. State-of-the-art workflows based on fully automatic geometry extraction are limited by point cloud density and memory constraints, and require pre- and post-processing by the user. In this work, we present a framework for interactive, user-driven, feature-assisted geometry reconstruction from arbitrarily sized point clouds. Based on seeded region-growing point cloud segmentation, the user interactively extracts planar pieces of geometry and utilizes contextual suggestions to point out plane surfaces, normal and tangential directions, and edges and corners. We implement a set of feature-assisted tools for high-precision modeling tasks in architecture and urban surveying scenarios, enabling instant-feedback interactive point cloud manipulation on large-scale data collected from real-world building interiors and facades. We evaluate our results through systematic measurement of the reconstruction accuracy, and interviews with domain experts who deploy our framework in a commercial setting and give both structured and subjective feedback.

翻译：从点云中重建几何形状是一项常见任务，通常需要专家在CAD软件中手动建模几何体。基于全自动几何提取的先进工作流受限于点云密度和内存约束，且需要用户进行预处理和后处理。本研究提出一种交互式、用户驱动、特征辅助的几何重建框架，适用于任意规模的点云。该方法基于种子区域生长的点云分割，用户可交互式提取平面几何片段，并利用上下文提示标定平面表面、法向与切向方向、以及边缘与角点。我们实现了一套特征辅助工具，用于建筑与城市测量场景中的高精度建模任务，能够对从真实建筑室内与立面采集的大规模数据进行即时反馈的交互式点云操作。通过系统化测量重建精度，并对在商业环境中部署该框架的领域专家进行访谈（获取结构化与主观反馈），我们评估了研究成果。