InfNeRF: Efficient Large-Scale Scene Rendering with O(log n) Space Complexity
Core Concepts
InfNeRF introduces an innovative approach to large-scale scene rendering using Neural Radiance Fields and octree structures, achieving efficient rendering with reduced memory footprint.
Abstract
InfNeRF extends Level of Detail (LoD) techniques to NeRF for scalable scene representation.
Utilizes octree structure for space and scale decomposition, reducing memory requirements.
Achieves superior rendering quality and anti-aliasing capabilities compared to existing methods.
Introduces tree pruning algorithm for efficient model sparsity and compactness.
Proposes a novel training strategy for InfNeRF with parallelization across machines.
InfNeRF
Stats
InfNeRF achieves superior rendering quality with an improvement of over 2.4dB in PSNR while accessing only 17% of the total parameters.
The memory complexity for rendering with InfNeRF is notably efficient at O(log n).
Quotes
"InfNeRF demonstrates its potential for large-scale scene reconstruction."
"In our experiments, InfNeRF achieves superior rendering quality."