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Instant Neural Graphics Primitives with a Multiresolution Hash Encoding: Difference between revisions
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding (view source)
Revision as of 17:36, 31 January 2022
, 31 January 2022→Multi-resolution Hash Encoding
(Created page with " [https://nvlabs.github.io/instant-ngp/ Project Webpage] Allows training a NeRF or other neural representation in seconds but optimizing features in a hash table.<br> This is faster than an octree since it doesn't require pointer chasing while using less space than enumerating voxels.<br> Furthermore, the size of the hash table allows control over the amount of detail. ==Method== ===Multi-resolution Hash Encoding=== ==Experiments== ==References== Category: Papers") |
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==Method== | ==Method== | ||
===Multi-resolution Hash Encoding=== | ===Multi-resolution Hash Encoding=== | ||
# For each level/resolution | |||
## Find which voxel we are in and get indices to the corners. | |||
## Hash each corner and retrieve features (<math>\in \mathbb{R}^{F}</math>) from the hash map. | |||
## Do trilienar interpolation to get an average feature. | |||
# Concatenate features from all levels (<math display="inline">\in \mathbb{R}^{LF}</math>) along with auxiliary inputs (<math display="inline">\in \mathbb{R}^{E}</math>, e.g. view direction) to produce a feature vector <math display="inline">\mathbf{y} \in \mathbb{R}^{LF+E}</math>. | |||
# Pass <math>\mathbf{y}</math> through your small feature decoding neural network. | |||
;Interesting details | |||
* Hash collisions are ignored. | |||
* The hash function used is: <math>h(\mathbf{x}) = \left( \bigoplus_{i=1}^{d} x_i \pi_i \right) \mod T</math> | |||
** <math>\bigoplus</math> is bitwise XOR. | |||
** <math>\pi_i</math> are unique large primes. | |||
** <math>T</math> is the size of the hash table. | |||
==Experiments== | ==Experiments== | ||
==References== | ==References== | ||
[[Category: Papers]] | [[Category: Papers]] | ||