We present Canonical Consolidation Fields (CanFields): a method for
reconstructing a time series of independently-sampled point clouds into a
single deforming coherent shape. Such input often comes from motion capture.
Existing methods either couple the geometry and the deformation, where by doing
so they smooth fine details and lose the ability to track moving points, or
they track the deformation explicitly, but introduce topological and geometric
artifacts. Our novelty lies in the consolidation of the point clouds into a
single canonical shape in a way that reduces the effect of noise and outliers,
and enables us to overcome missing regions. We simultaneously reconstruct the
velocity fields that guide the deformation. This consolidation allows us to
retain the high-frequency details of the geometry, while faithfully reproducing
the low-frequency deformation. Our architecture comprises simple components,
and fits any single input shape without using datasets. We demonstrate the
robustness and accuracy of our methods on a diverse benchmark of dynamic point
clouds, including missing regions, sparse frames, and noise.

我们提出了规范合并场（CanFields）：一种将独立采样的时间序列点云重建为单一变形的连贯形状的方法。我们的方法通过将点云合并成一个规范形状来减少噪声和离群值的影响，并且允许我们处理缺失的区域。通过同时重构引导变形的速度场，我们保留了几何的高频细节，同时忠实地再现了低频变形。我们的架构具有简单的组件，并适用于任何单个输入形状，不需要使用数据集。我们在包括缺失区域、稀疏帧和噪声在内的各种动态点云基准上展示了我们方法的鲁棒性和准确性。