In engineering applications, line, circle, arc, and point are collectively
referred to as primitives, and they play a crucial role in path planning,
simulation analysis, and manufacturing. When designing CAD models, engineers
typically start by sketching the model's orthographic view on paper or a
whiteboard and then translate the design intent into a CAD program. Although
this design method is powerful, it often involves challenging and repetitive
tasks, requiring engineers to perform numerous similar operations in each
design. To address this conversion process, we propose an efficient and
accurate end-to-end method that avoids the inefficiency and error accumulation
issues associated with using auto-regressive models to infer parametric
primitives from hand-drawn sketch images. Since our model samples match the
representation format of standard CAD software, they can be imported into CAD
software for solving, editing, and applied to downstream design tasks.

提出一种高效准确的端到端方法，避免使用自回归模型从手绘草图图像中推断参数化基元的效率低下和误差累积问题，生成的模型样本符合标准 CAD 软件的表示格式，可供 CAD 软件进行求解、编辑和用于下游设计任务。

PPI-NET: 端到端参数化原始推理

PPI-NET: End-to-End Parametric Primitive Inference

We describe a method to parse a complex, cluttered indoor scene into
primitives which offer a parsimonious abstraction of scene structure. Our
primitives are simple convexes. Our method uses a learned regression procedure
to parse a scene into a fixed number of convexes from RGBD input, and can
optionally accept segmentations to improve the decomposition. The result is
then polished with a descent method which adjusts the convexes to produce a
very good fit, and greedily removes superfluous primitives. Because the entire
scene is parsed, we can evaluate using traditional depth, normal, and
segmentation error metrics. Our evaluation procedure demonstrates that the
error from our primitive representation is comparable to that of predicting
depth from a single image.

描述了一种将复杂、混乱的室内场景解析为简化的基本结构的方法。这种方法使用学习回归过程将场景从 RGBD 输入解析为固定数量的凸多边形，并可以选择性地接受分割以改善分解。然后使用下降方法调整凸多边形以获得很好的拟合度，并贪婪地去除冗余的基本结构。因为对整个场景进行了解析，所以可以使用传统的深度、法线和分割误差指标进行评估。评估过程表明，我们的基本结构表示的误差与从单个图像预测深度的误差相当。

室内场景的凸分解

Convex Decomposition of Indoor Scenes

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.

通过在实现中使用用户提供的一组三维部件的库，我们提出了基于自我监督的方法来将 3D 形状分解，使得对部件的选择和分解方式都可以进行完全控制。实验表明，这种方法比现有方法提供了更高的重构准确性和更理想的分解。