MCBlock: Boosting Neural Radiance Field Training Speed by MCTS-based Dynamic-Resolution Ray Sampling

2025-04-14

AI Summary

NeRF training suffers from low efficiency, particularly in latency-critical applications such as telemedicine. Existing ray sampling strategies treat all image regions uniformly, ignoring local texture complexity and thus wasting GPU resources. To address this, we propose a dynamic-resolution ray sampling method guided by Monte Carlo Tree Search (MCTS)—the first integration of MCTS into the NeRF training pipeline. Our approach enables texture-aware, adaptive partitioning, real-time expansion, and pruning of pixel blocks based on local texture intensity, thereby overcoming the efficiency bottleneck imposed by fixed-resolution sampling. Integrated within the Nerfstudio framework, our method accelerates standard NeRF training by 2.33× compared to baseline, outperforming state-of-the-art ray sampling algorithms. Moreover, it is fully compatible with cone-traced NeRF variants, including those employing volumetric rendering with directional cones.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Hardware-aware ML
• Computer Vision: Motion & Tracking

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Improves NeRF training speed via dynamic ray sampling
• Optimizes GPU resource use with adaptive texture-based sampling
• Enables faster multimedia applications like telemedicine

Innovation

• MCTS-based dynamic-resolution ray sampling
• Texture-adaptive block partitioning
• Expansion/pruning module for optimization