We present a learning-based method for interpolating and manipulating 3D shapes represented as point clouds, that is explicitly designed to preserve intrinsic shape properties. Our approach is based on constructing a dual encoding space that enables shape synthesis and, at the same time, provides links to the intrinsic shape information, which is typically not available on point cloud data. Our method works in a single pass and avoids expensive optimization, employed by existing techniques. Furthermore, the strong regularization provided by our dual latent space approach also helps to improve shape recovery in challenging settings from noisy point clouds across different datasets. Extensive experiments show that our method results in more realistic and smoother interpolations compared to baselines.

中文翻译：

---

通过双潜在空间导航的内在点云插值

我们提出了一种基于学习的方法，用于内插和操作表示为点云的 3D 形状，该方法明确设计为保留固有的形状属性。我们的方法基于构建一个双编码空间，该空间可以实现形状合成，同时提供指向固有形状信息的链接，这通常在点云数据上不可用。我们的方法一次性工作，避免了现有技术采用的昂贵优化。此外，我们的双潜在空间方法提供的强正则化也有助于改善具有挑战性的环境中不同数据集的嘈杂点云的形状恢复。大量实验表明，与基线相比，我们的方法产生了更真实、更平滑的插值。