通过多阶段多层级查询优化，提升点云重建的细节保真度。
MultiPull: Detailing Signed Distance Functions by Pulling Multi-Level Queries at Multi-Step
- 分阶段用多层级查询优化隐式场，逐步细化表面细节。
- 在多个基准数据集上，重建精度超越当前最优方法。
- 适合需要高保真几何重建的3D建模与逆向工程场景。
从原始3D点云重构连续表面是一项挑战性任务。现有方法通常训练神经网络对单个点云过拟合以推断有符号距离函数（SDF）。然而，由于缺乏真实SDF或法线信息，神经网络倾向于平滑局部细节，限制了过拟合方法在重建任务中的表现。为此，我们提出一种新方法MultiPull，通过从粗到细的多尺度优化策略，从原始点云中学习多尺度隐式场。该方法将3D查询点映射到一组频域特征，实现优化过程中多层级特征的利用。同时，引入空间距离和法线一致性约束，显著提升基于多尺度优化的点云重建效果。在广泛使用的物体与场景基准数据集上的实验表明，本方法在表面重建性能上优于当前最先进方法。
原文摘要 · Abstract (English)
Reconstructing a continuous surface from a raw 3D point cloud is a challenging task. Recent methods usually train neural networks to overfit on single point clouds to infer signed distance functions (SDFs). However, neural networks tend to smooth local details due to the lack of ground truth signed distances or normals, which limits the performance of overfitting-based methods in reconstruction tasks. To resolve this issue, we propose a novel method, named MultiPull, to learn multi-scale implicit fields from raw point clouds by optimizing accurate SDFs from coarse to fine. We achieve this by mapping 3D query points into a set of frequency features, which makes it possible to leverage multi-level features during optimization. Meanwhile, we introduce optimization constraints from the perspective of spatial distance and normal consistency, which play a key role in point cloud reconstruction based on multi-scale optimization strategies. Our experiments on widely used object and scene benchmarks demonstrate that our method outperforms the state-of-the-art methods in surface reconstruction.
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