Representing a 3D shape with a set of primitives can aid perception of structure, improve robotic object manipulation, and enable editing, stylization, and compression of 3D shapes. Existing methods either use simple parametric primitives or learn a generative shape space of parts. Both have limitations: parametric primitives lead to coarse approximations, while learned parts offer too little control over the decomposition. We instead propose to decompose shapes using a library of 3D parts provided by the user, giving full control over the choice of parts. The library can contain parts with high-quality geometry that are suitable for a given category, resulting in meaningful decompositions with clean geometry. The type of decomposition can also be controlled through the choice of parts in the library. Our method works via a self-supervised approach that iteratively retrieves parts from the library and refines their placements. We show that this approach gives higher reconstruction accuracy and more desirable decompositions than existing approaches. Additionally, we show how the decomposition can be controlled through the part library by using different part libraries to reconstruct the same shapes.

翻译：使用一组基元表示三维形状有助于感知结构、改进机器人对物体的操作，并支持三维形状的编辑、风格化和压缩。现有方法要么使用简单的参数化基元，要么学习部件的生成形状空间。这两种方法都有局限性：参数化基元会导致粗糙的近似，而学习到的部件对分解的操控能力过弱。我们提出利用用户提供的三维部件库来分解形状，从而对部件选择实现完全控制。该部件库可包含针对特定类别的高质量几何形状部件，从而产生具有清晰几何结构的有意义的分解。通过选择库中的部件，还可以控制分解的类型。我们的方法通过一种自监督方式实现，该方式迭代地从库中检索部件并优化其放置位置。研究表明，与现有方法相比，该方法能够获得更高的重建精度和更理想的分解效果。此外，我们还展示了如何通过使用不同的部件库重建相同形状来控制分解过程。