GeoCode: Interpretable Shape Programs
GeoCode: Interpretable Shape Programs
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DOI：
10.48550/arxiv.2212.11715
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Ofek Pearl;Itai Lang;Yu Hu;Raymond A. Yeh;Rana Hanocka
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作者：
Ofek Pearl;Itai Lang;Yu Hu;Raymond A. Yeh;Rana Hanocka
将高保真3D几何图形映射到允许直观编辑的表示仍然是计算机视觉和图形中难以实现的目标。关键的挑战是需要对连续和离散的形状变化进行建模。目前的方法，如隐式形状表示，缺乏直接的可解释的编码，而其他采用过程方法输出粗糙的几何形状。我们提出了地理编码，一种技术，用于三维形状合成使用一个直观的可编辑的参数空间。我们建立了一个新的程序，强制执行一套复杂的规则，使用户能够执行直观和控制的高层次的编辑，程序传播在一个低层次的整个形状。我们的程序通过构造产生高质量的网格输出。我们使用神经网络将给定的点云或草图映射到我们可解释的参数空间。一旦由我们的程序程序产生，形状可以很容易地修改。经验上，我们表明，地理编码可以推断和恢复3D形状更准确地与现有的技术相比，我们证明了它的能力，执行控制的本地和全球的形状操作。
Mapping high-fidelity 3D geometry to a representation that allows for intuitive edits remains an elusive goal in computer vision and graphics. The key challenge is the need to model both continuous and discrete shape variations. Current approaches, such as implicit shape representation, lack straightforward interpretable encoding, while others that employ procedural methods output coarse geometry. We present GeoCode, a technique for 3D shape synthesis using an intuitively editable parameter space. We build a novel program that enforces a complex set of rules and enables users to perform intuitive and controlled high-level edits that procedurally propagate at a low level to the entire shape. Our program produces high-quality mesh outputs by construction. We use a neural network to map a given point cloud or sketch to our interpretable parameter space. Once produced by our procedural program, shapes can be easily modified. Empirically, we show that GeoCode can infer and recover 3D shapes more accurately compared to existing techniques and we demonstrate its ability to perform controlled local and global shape manipulations.