In this paper, we study the problem of multi-view sketch correspondence, where we take as input multiple freehand sketches with different views of the same object and predict semantic correspondence among the sketches. This problem is challenging, since visual features of corresponding points at different views can be very different. To this end, we take a deep learning approach and learn a novel local sketch descriptor from data. We contribute a training dataset by generating the pixel-level correspondence for the multi-view line drawings synthesized from 3D shapes. To handle the sparsity and ambiguity of sketches, we design a novel multi-branch neural network that integrates a patch-based representation and a multi-scale strategy to learn the \pixelLevel correspondence among multi-view sketches. We demonstrate the effectiveness of our proposed approach with extensive experiments on hand-drawn sketches, and multi-view line drawings rendered from multiple 3D shape datasets.

中文翻译：

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SketchDesc：为多视图对应学习局部草图描述符

在本文中，我们研究了多视图草图对应问题，我们将具有同一对象不同视图的多个手绘草图作为输入，并预测草图之间的语义对应关系。这个问题具有挑战性，因为不同视图中对应点的视觉特征可能非常不同。为此，我们采用深度学习方法，从数据中学习新的局部草图描述符。我们通过为从 3D 形状合成的多视图线条图生成像素级对应关系来提供训练数据集。为了处理草图的稀疏性和歧义性，我们设计了一种新颖的多分支神经网络，该网络集成了基于补丁的表示和多尺度策略，以学习多视图草图之间的 \pixelLevel 对应关系。