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Encodes each input dimension using a cheap-to-compute triangle wave at multiple frequencies.
Distinct from Input Encoding Combinations: Distinct from Input Encoding Combinations: focuses on the specific triangle wave encoding technique, not the combination of multiple encoding schemes.
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This project is a high-performance C++ and CUDA neural network library designed for fast training and inference of small networks on NVIDIA GPUs. It serves as a specialized backend for neural radiance fields and coordinate-based networks, providing a fused GPU kernel library and a hash grid encoder for transforming raw input dimensions into high-dimensional representations. The library distinguishes itself through the use of C++ template metaprogramming and fused-kernel execution, which merge neural network layers into single GPU device functions to eliminate memory bottlenecks. It leverages
Encodes each input dimension using a cheap-to-compute triangle wave at multiple frequencies.