We introduce PC2WF, the first end-to-end trainable deep network architecture to convert a 3D point cloud into a wireframe model. The network takes as input an unordered set of 3D points sampled from the surface of some object, and outputs a wireframe of that object, i.e., a sparse set of corner points linked by line segments. Recovering the wireframe is a challenging task, where the numbers of both vertices and edges are different for every instance, and a-priori unknown. Our architecture gradually builds up the model: It starts by encoding the points into feature vectors. Based on those features, it identifies a pool of candidate vertices, then prunes those candidates to a final set of corner vertices and refines their locations. Next, the corners are linked with an exhaustive set of candidate edges, which is again pruned to obtain the final wireframe. All steps are trainable, and errors can be backpropagated through the entire sequence. We validate the proposed model on a publicly available synthetic dataset, for which the ground truth wireframes are accessible, as well as on a new real-world dataset. Our model produces wireframe abstractions of good quality and outperforms several baselines.

中文翻译：

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PC2WF：从原始点云进行3D线框重构

我们介绍PC2WF，这是第一个将3D点云转换为线框模型的端到端可训练深度网络体系结构。网络将从某个对象的表面采样的一组无序3D点作为输入，并输出该对象的线框，即由线段链接的稀疏角点集。恢复线框是一项具有挑战性的任务，其中每个实例的顶点和边的数量都不同，并且先验未知。我们的架构逐步建立模型：首先将点编码为特征向量。基于这些功能，它会识别候选顶点池，然后将这些候选元素修剪成最终的一组角顶点并优化其位置。接下来，将边角与一组详尽的候选边链接起来，再次对其进行修剪以获得最终的线框。所有步骤都是可训练的，并且错误可以在整个序列中反向传播。我们在可访问地面真实线框的公共合成数据集上以及在新的现实世界数据集上验证提出的模型。我们的模型可以产生高质量的线框抽象，并且性能优于多个基准。