title: Compact Neural Graphics Primitives with Learned Hash Probing
summary: 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.
paper_authors._id: 658e65ab57a556fbe1587c7f
paper_authors.name: Towaki Takikawa
paper_authors._id: 658e65ab57a556fbe1587c80
paper_authors.name: Thomas Müller
paper_authors._id: 658e65ab57a556fbe1587c81
paper_authors.name: Merlin Nimier-David
paper_authors._id: 658e65ab57a556fbe1587c82
paper_authors.name: Alex Evans
paper_authors._id: 658e65ab57a556fbe1587c83
paper_authors.name: Sanja Fidler
paper_authors._id: 658e65ab57a556fbe1587c84
paper_authors.name: Alec Jacobson
paper_authors._id: 658e65ab57a556fbe1587c85
paper_authors.name: Alexander Keller
paper_title: Compact Neural Graphics Primitives with Learned Hash Probing
paper_summary: 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.
paper_ai_keywords: neural graphics primitives spatial data structures trainable features feature grids dense grids factorized grids hash tables learned probes memory footprint index learning vector quantization lookup function feature vectors Pareto optimal compression