Tri-MipRF: Tri-Mip Representation for Efficient Anti-Aliasing Neural
  Radiance Fields
Wenbo Hu Yuling Wang Lin Ma Bangbang Yang Lin Gao Xiao Liu Yuewen Ma
Despite the tremendous progress in neural radiance fields (NeRF), we still
face a dilemma of the trade-off between quality and efficiency, e.g., MipNeRF
presents fine-detailed and anti-aliased renderings but takes days for training,
while Instant-ngp can accomplish the reconstruction in a few minutes but
suffers from blurring or aliasing when rendering at various distances or
resolutions due to ignoring the sampling area. To this end, we propose a novel
Tri-Mip encoding that enables both instant reconstruction and anti-aliased
high-fidelity rendering for neural radiance fields. The key is to factorize the
pre-filtered 3D feature spaces in three orthogonal mipmaps. In this way, we can
efficiently perform 3D area sampling by taking advantage of 2D pre-filtered
feature maps, which significantly elevates the rendering quality without
sacrificing efficiency. To cope with the novel Tri-Mip representation, we
propose a cone-casting rendering technique to efficiently sample anti-aliased
3D features with the Tri-Mip encoding considering both pixel imaging and
observing distance. Extensive experiments on both synthetic and real-world
datasets demonstrate our method achieves state-of-the-art rendering quality and
reconstruction speed while maintaining a compact representation that reduces
25% model size compared against Instant-ngp.