Spatially-Adaptive Hash Encodings For Neural Surface Reconstruction
This addresses the limitation of fixed encodings in scene reconstruction for computer vision and graphics, offering an incremental improvement over existing methods.
The paper tackles the problem of neural surface reconstruction by proposing a learned spatially-adaptive hash encoding that allows networks to choose encoding bases as a function of space, achieving state-of-the-art performance on two benchmark datasets.
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.