A heat-equation-based loss for neural signed distance functions gives an asymptotically sufficient condition for convergence to the true distance, with better surface and distance accuracy on shape benchmarks.
Sal: Sign agnostic learn- ing of shapes from raw data
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HotSpot: Signed Distance Function Optimization with an Asymptotically Sufficient Condition
A heat-equation-based loss for neural signed distance functions gives an asymptotically sufficient condition for convergence to the true distance, with better surface and distance accuracy on shape benchmarks.