Learning radiance fields has shown remarkable results for novel view synthesis. The learning procedure usually costs lots of time, which motivates the latest methods to speed up the learning procedure by learning without neural networks or using more efficient data structures. However, these specially designed approaches do not work for most of radiance fields based methods. To resolve this issue, we introduce a general strategy to speed up the learning procedure for almost all radiance fields based methods. Our key idea is to reduce the redundancy by shooting much fewer rays in the multi-view volume rendering procedure which is the base for almost all radiance fields based methods. We find that shooting rays at pixels with dramatic color change not only significantly reduces the training burden but also barely affects the accuracy of the learned radiance fields. In addition, we also adaptively subdivide each view into a quadtree according to the average rendering error in each node in the tree, which makes us dynamically shoot more rays in more complex regions with larger rendering error. We evaluate our method with different radiance fields based methods under the widely used benchmarks. Experimental results show that our method achieves comparable accuracy to the state-of-the-art with much faster training.

翻译：学习辐射场在新视角合成领域已展现出显著成果。然而，训练过程通常耗时巨大，这促使最新方法通过无神经网络学习或采用更高效的数据结构来加速训练。但这些专门设计的方法对大多数基于辐射场的方法并不适用。为解决此问题，我们提出一种通用策略，可加速几乎所有基于辐射场方法的训练过程。我们的核心思想是通过在多视角体渲染过程中大幅减少光线投射数量来降低冗余——该过程是几乎所有辐射场方法的基石。研究发现，在颜色变化剧烈的像素处投射光线不仅能显著减轻训练负担，且几乎不影响学习辐射场的精度。此外，我们根据每个节点中的平均渲染误差将每个视图自适应地细分为四叉树，从而在渲染误差较大的复杂区域动态投射更多光线。我们在广泛使用的基准测试中，采用不同的辐射场方法评估了本方案。实验结果表明，我们的方法在实现与最先进技术相当精度的同时，训练速度显著提升。