MCBlock: Boosting Neural Radiance Field Training Speed by MCTS-based Dynamic-Resolution Ray Sampling
Journal:
arXiv
Published Date:
Apr 14, 2025
Abstract
Neural Radiance Field (NeRF) is widely known for high-fidelity novel view
synthesis. However, even the state-of-the-art NeRF model, Gaussian Splatting,
requires minutes for training, far from the real-time performance required by
multimedia scenarios like telemedicine. One of the obstacles is its inefficient
sampling, which is only partially addressed by existing works. Existing
point-sampling algorithms uniformly sample simple-texture regions (easy to fit)
and complex-texture regions (hard to fit), while existing ray-sampling
algorithms sample these regions all in the finest granularity (i.e. the pixel
level), both wasting GPU training resources. Actually, regions with different
texture intensities require different sampling granularities. To this end, we
propose a novel dynamic-resolution ray-sampling algorithm, MCBlock, which
employs Monte Carlo Tree Search (MCTS) to partition each training image into
pixel blocks with different sizes for active block-wise training. Specifically,
the trees are initialized according to the texture of training images to boost
the initialization speed, and an expansion/pruning module dynamically optimizes
the block partition. MCBlock is implemented in Nerfstudio, an open-source
toolset, and achieves a training acceleration of up to 2.33x, surpassing other
ray-sampling algorithms. We believe MCBlock can apply to any cone-tracing NeRF
model and contribute to the multimedia community.
