让神经表面重建自动选择不同空间的编码方式，提升细节表现力。
Spatially-Adaptive Hash Encodings For Neural Surface Reconstruction
- 根据空间位置动态选择不同分辨率的编码特征
- 在两个基准数据集上达到当前最佳性能
- 适合需要高精度几何重建的研究与应用
位置编码是神经场景重建方法中的常见组件，可引导神经场学习粗粒度或细粒度表示。现有方法采用全局固定的编码函数，对所有场景统一使用相同编码偏置。当前最先进的表面重建方法使用基于网格的多分辨率哈希编码以恢复高细节几何。本文提出一种可学习的自适应方法，使网络能根据空间位置动态屏蔽不同分辨率网格存储的特征贡献，实现空间自适应编码。该方法可在不引入噪声的情况下拟合更广泛的频率成分。我们在标准表面重建基准数据集上进行测试，在两个数据集上均取得当前最优性能。
原文摘要 · Abstract (English)
Positional encodings are a common component of neural scene reconstruction methods, and provide a way to bias the learning of neural fields towards coarser or finer representations. Current neural surface reconstruction methods use a "one-size-fits-all" approach to encoding, choosing a fixed set of encoding functions, and therefore bias, across all scenes. Current state-of-the-art surface reconstruction approaches leverage grid-based multi-resolution hash encoding in order to recover high-detail geometry. We propose a learned approach which allows the network to choose its encoding basis as a function of space, by masking the contribution of features stored at separate grid resolutions. The resulting spatially adaptive approach allows the network to fit a wider range of frequencies without introducing noise. We test our approach on standard benchmark surface reconstruction datasets and achieve state-of-the-art performance on two benchmark datasets.
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