We present Strokes2Surface, an offline geometry reconstruction pipeline that recovers well-connected curve networks from imprecise 4D sketches to bridge concept design and digital modeling stages in architectural design. The input to our pipeline consists of 3D strokes' polyline vertices and their timestamps as the 4th dimension, along with additional metadata recorded throughout sketching. Inspired by architectural sketching practices, our pipeline combines a classifier and two clustering models to achieve its goal. First, with a set of extracted hand-engineered features from the sketch, the classifier recognizes the type of individual strokes between those depicting boundaries (Shape strokes) and those depicting enclosed areas (Scribble strokes). Next, the two clustering models parse strokes of each type into distinct groups, each representing an individual edge or face of the intended architectural object. Curve networks are then formed through topology recovery of consolidated Shape clusters and surfaced using Scribble clusters guiding the cycle discovery. Our evaluation is threefold: We confirm the usability of the Strokes2Surface pipeline in architectural design use cases via a user study, we validate our choice of features via statistical analysis and ablation studies on our collected dataset, and we compare our outputs against a range of reconstructions computed using alternative methods.

翻译：我们提出Strokes2Surface，一种离线几何重建流程，旨在从非精确的四维草图中恢复良好连接的曲线网络，从而在建筑设计中桥接概念设计与数字建模阶段。该流程的输入包含三维折线顶点及其作为第四维度的时间戳，以及草图绘制过程中记录的额外元数据。受建筑设计实践启发，该流程结合了一个分类器和两个聚类模型来实现目标。首先，利用从草图中提取的一组手工设计的特征，分类器识别出描绘边界的笔画（形状笔画）与描绘封闭区域的笔画（涂鸦笔画）。随后，两个聚类模型将每种类型的笔画解析为不同组别，分别对应目标建筑对象的独立边或面。通过恢复整合后的形状聚类的拓扑结构形成曲线网络，并利用涂鸦聚类引导的环检测进行曲面生成。我们的评估分为三个方面：通过用户研究确认了Strokes2Surface流程在建筑设计用例中的可用性；通过统计分析和在所收集数据集上的消融研究验证了特征选择的合理性；并将输出结果与一系列基于替代方法计算的重建结果进行了对比。