Compact Neural Graphics Primitives with Learned Hash Probing
Neural graphics primitives are faster and achieve higher quality when their
neural networks are augmented by spatial data structures that hold trainable
features arranged in a grid. However, existing feature grids either come with a
large memory footprint (dense or factorized grids, trees, and hash tables) or
slow performance (index learning and vector quantization). In this paper, we
show that a hash table with learned probes has neither disadvantage, resulting
in a favorable combination of size and speed. Inference is faster than unprobed
hash tables at equal quality while training is only 1.2-2.6x slower,
significantly outperforming prior index learning approaches. We arrive at this
formulation by casting all feature grids into a common framework: they each
correspond to a lookup function that indexes into a table of feature vectors.
In this framework, the lookup functions of existing data structures can be
combined by simple arithmetic combinations of their indices, resulting in
Pareto optimal compression and speed.
658e65ab57a556fbe1587c7f
Towaki Takikawa
658e65ab57a556fbe1587c80
Thomas Müller
658e65ab57a556fbe1587c81
Merlin Nimier-David
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2025-07-04T14:10:09.453Z
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/avatars/e042c25d55f9ef560fc0c2de51188b2d.svg
Merlin Nimier-David
user
merlinND
658e65ab57a556fbe1587c82
Alex Evans
658e65ab57a556fbe1587c83
Sanja Fidler
658e65ab57a556fbe1587c84
Alec Jacobson
658e65ab57a556fbe1587c85
Alexander Keller
Compact Neural Graphics Primitives with Learned Hash Probing
Neural graphics primitives are faster and achieve higher quality when their
neural networks are augmented by spatial data structures that hold trainable
features arranged in a grid. However, existing feature grids either come with a
large memory footprint (dense or factorized grids, trees, and hash tables) or
slow performance (index learning and vector quantization). In this paper, we
show that a hash table with learned probes has neither disadvantage, resulting
in a favorable combination of size and speed. Inference is faster than unprobed
hash tables at equal quality while training is only 1.2-2.6x slower,
significantly outperforming prior index learning approaches. We arrive at this
formulation by casting all feature grids into a common framework: they each
correspond to a lookup function that indexes into a table of feature vectors.
In this framework, the lookup functions of existing data structures can be
combined by simple arithmetic combinations of their indices, resulting in
Pareto optimal compression and speed.
Hash tables with learned probes achieve high quality and fast inference for neural graphics primitives without the drawbacks of existing feature grids.
neural graphics primitives spatial data structures feature grids dense grids factorized grids trees hash tables learned probes lookup functions feature vectors index learning vector quantization Pareto optimal compression