The quantification of 3-D shape aesthetics has so far focused on specific shape features and manually defined criteria such as the curvature and the rule of thirds. In this article, we built a model of 3-D shape aesthetics directly from human aesthetics preference data and show it to be well aligned with human perception of aesthetics. To build this model, we first crowdsource a large number of human aesthetics preferences by showing shapes in pairs in an online study and then use the same to build a 3-D shape multiview-based deep neural network architecture to allow us to learn a measure of 3-D shape aesthetics. In comparison to previous approaches, we do not use any predefined notions of aesthetics to build our model. Our algorithmically computed measure of shape aesthetics is beneficial to a range of applications in graphics such as search, visualization, and scene composition.

中文翻译：

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从多个视图学习 3-D 形状的感知美学

迄今为止，3-D 形状美学的量化主要集中在特定的形状特征和手动定义的标准上，例如曲率和三分法则。在本文中，我们直接从人类审美偏好数据中构建了一个 3-D 形状美学模型，并表明它与人类对审美的感知非常吻合。为了构建这个模型，我们首先通过在线研究中成对显示形状来众包大量人类审美偏好，然后使用相同的方法构建基于 3-D 形状多视图的深度神经网络架构，让我们能够学习度量3-D 形状美学。与以前的方法相比，我们不使用任何预定义的美学概念来构建我们的模型。我们的算法计算的形状美学测量有利于图形中的一系列应用，例如搜索、