Neu-PiG：神经预条件网格用于长序列快速动态表面重建
Neu-PiG: Neural Preconditioned Grids for Fast Dynamic Surface Reconstruction on Long Sequences
提出一种基于预条件潜变量网格的快速形变优化方法，无需显式对应点即可从点云序列重建动态表面，其Sobolev预条件训练策略可推广到其他需要高效优化潜变量的三维重建任务。
原文依据 we employ Sobolev preconditioning during gradient-based training of the latent space, completely avoiding the need for any explicit correspondences or further priors.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论从非结构化点云数据中重建动态三维物体的时间一致表面仍然具有挑战性，尤其是对于非常长的序列。现有方法要么增量优化形变，存在漂移风险且需要长时间运行，要么依赖需要类别特定训练的复杂学习模型。我们提出Neu-PiG，一种基于新型预条件潜变量网格编码的快速形变优化方法，该编码在关键帧表面的位置和法线方向上分布空间特征。我们的方法将各种空间尺度上所有时间步的整个形变编码到一个多分辨率潜变量网格中，该网格由来自单个关键帧的参考表面的位置和法线方向参数化。然后，该潜变量表示被增强以进行时间调制，并通过轻量级多层感知机（MLP）解码为每帧的6自由度形变。为了在几秒内实现高保真、无漂移的表面重建，我们在潜变量空间的基于梯度的训练中采用Sobolev预条件，完全避免了任何显式对应或进一步先验的需要。在多种人类和动物数据集上的实验表明，Neu-PiG优于最先进的方法，在提供更高精度和对长序列的可扩展性的同时，运行速度至少比现有的无训练方法快60倍，并达到与重型预训练模型相同数量级的推理速度。
英文原文摘要 1507 字符
Temporally consistent surface reconstruction of dynamic 3D objects from unstructured point cloud data remains challenging, especially for very long sequences. Existing methods either optimize deformations incrementally, risking drift and requiring long runtimes, or rely on complex learned models that demand category-specific training. We present Neu-PiG, a fast deformation optimization method based on a novel preconditioned latent-grid encoding that distributes spatial features parameterized on the position and normal direction of a keyframe surface. Our method encodes entire deformations across all time steps at various spatial scales into a multi-resolution latent grid, parameterized by the position and normal direction of a reference surface from a single keyframe. This latent representation is then augmented for time modulation and decoded into per-frame 6-DoF deformations via a lightweight multi-layer perceptron (MLP). To achieve high-fidelity, drift-free surface reconstructions in seconds, we employ Sobolev preconditioning during gradient-based training of the latent space, completely avoiding the need for any explicit correspondences or further priors. Experiments across diverse human and animal datasets demonstrate that Neu-PiG outperforms state-the-art approaches, offering both superior accuracy and scalability to long sequences while running at least 60x faster than existing training-free methods and achieving inference speeds on the same order as heavy pre-trained models.