Spatially-Adaptive Hash Encodings For Neural Surface Reconstruction

• URL: http://arxiv.org/abs/2412.05179v1
• Date: Fri, 06 Dec 2024 16:54:55 GMT
• Title: Spatially-Adaptive Hash Encodings For Neural Surface Reconstruction
• Authors: Thomas Walker, Octave Mariotti, Amir Vaxman, Hakan Bilen,
• Abstract summary: We propose a learned approach which allows the network to choose its encoding basis as a function of space. The resulting spatially adaptive approach allows the network to fit a wider range of frequencies without introducing noise.
• Score: 30.561368257031393
• License:
• Abstract: 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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