Automatic Differentiable Procedural Modeling
Automatic Differentiable Procedural Modeling
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DOI：
10.1111/cgf.14475
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发表时间：
2022-05
影响因子：
2.5
通讯作者：
Mathieu Gaillard;Vojtech Krs;Giorgio Gori;R. Mech;Bedrich Benes
中科院分区：
文献类型：
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作者：
Mathieu Gaillard;Vojtech Krs;Giorgio Gori;R. Mech;Bedrich Benes
程序建模允许自动生成大量相似的资产，但对生成的输出的控制有限。我们通过引入自动可微过程建模（ADPM）来解决这个问题。正向过程模型生成最终的可编辑模型。用户交互地修改输出，并且通过解决逆过程建模问题将修改作为其参数传输回过程模型。我们提出了程序模型的自动可微表示，可以显着加速优化。在 ADPM 中，程序模型始终可用，所有更改都是非破坏性的，用户可以交互式地对 3D 对象进行建模，同时保留程序表示。 ADPM 为用户提供了对结果模型的精确控制，与非过程交互式建模相当。 ADPM 是基于节点的，它生成分层的 3D 场景几何图形，转换为可微的计算图。我们的公式侧重于程序模型组件的高级基元和包围体的可微性，而不是详细的网格几何形状。尽管这种高级公式限制了用户编辑的表现力，但它允许高效的导数计算并实现交互性。我们设计了一个新的优化器来解决逆过程建模。它可以检测到编辑是不确定的并且具有自由度。利用廉价的导数评估，它可以探索编辑的最优区域并建议各种配置，所有这些配置都以不同的方式实现所需的编辑。我们通过几个示例展示了我们系统的效率，并通过用户研究对其进行了验证。
Procedural modeling allows for an automatic generation of large amounts of similar assets, but there is limited control over the generated output. We address this problem by introducing Automatic Differentiable Procedural Modeling (ADPM). The forward procedural model generates a final editable model. The user modifies the output interactively, and the modifications are transferred back to the procedural model as its parameters by solving an inverse procedural modeling problem. We present an auto‐differentiable representation of the procedural model that significantly accelerates optimization. In ADPM the procedural model is always available, all changes are non‐destructive, and the user can interactively model the 3D object while keeping the procedural representation. ADPM provides the user with precise control over the resulting model comparable to non‐procedural interactive modeling. ADPM is node‐based, and it generates hierarchical 3D scene geometry converted to a differentiable computational graph. Our formulation focuses on the differentiability of high‐level primitives and bounding volumes of components of the procedural model rather than the detailed mesh geometry. Although this high‐level formulation limits the expressiveness of user edits, it allows for efficient derivative computation and enables interactivity. We designed a new optimizer to solve for inverse procedural modeling. It can detect that an edit is under‐determined and has degrees of freedom. Leveraging cheap derivative evaluation, it can explore the region of optimality of edits and suggest various configurations, all of which achieve the requested edit differently. We show our system's efficiency on several examples, and we validate it by a user study.