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.

翻译：位置编码是神经场景重建方法中的常见组件，它提供了一种将神经场学习偏向于更粗糙或更精细表示的方式。当前的神经表面重建方法采用"一刀切"的编码方法，即选择一组固定的编码函数，从而在所有场景中施加相同的偏置。当前最先进的表面重建方法利用基于网格的多分辨率哈希编码来恢复高细节几何。我们提出一种学习方法，通过屏蔽存储在不同网格分辨率下的特征的贡献，使网络能够根据空间位置选择其编码基。这种空间自适应方法使网络能够拟合更广泛的频率范围，而不会引入噪声。我们在标准基准表面重建数据集上测试了我们的方法，并在两个基准数据集上实现了最先进的性能。