SAND: Spatially Adaptive Network Depth for Fast Sampling of Neural Implicit Surfaces
Implicit neural representations are powerful for geometric modeling, but their practical use is often limited by the high computational cost of network evaluations. We observe that implicit representations require progressively lower accuracy as query points move farther from the target surface, and that even within the same iso-surface, representation difficulty varies spatially with local geometric complexity. However, conventional neural implicit models evaluate all query points with the same network depth and computational cost, ignoring this spatial variation and thereby incurring substantial computational waste. Motivated by this observation, we propose an efficient neural implicit ...
| Authors | Chuanxiang Yang, Junhui Hou, Yuan Liu et al. |
|---|---|
| Primary category | cs.GR |
| Published | 15 April 2026 |
| Last updated at source | 15 April 2026 |
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