Modern Neural Radiance Fields (NeRFs) learn a mapping from position to volumetric density via proposal network samplers. In contrast to the coarse-to-fine sampling approach with two NeRFs, this offers significant potential for speedups using lower network capacity as the task of mapping spatial coordinates to volumetric density involves no view-dependent effects and is thus much easier to learn. Given that most of the network capacity is utilized to estimate radiance, NeRFs could store valuable density information in their parameters or their deep features. To this end, we take one step back and analyze large, trained ReLU-MLPs used in coarse-to-fine sampling. We find that trained NeRFs, Mip-NeRFs and proposal network samplers map samples with high density to local minima along a ray in activation feature space. We show how these large MLPs can be accelerated by transforming the intermediate activations to a weight estimate, without any modifications to the parameters post-optimization. With our approach, we can reduce the computational requirements of trained NeRFs by up to 50% with only a slight hit in rendering quality and no changes to the training protocol or architecture. We evaluate our approach on a variety of architectures and datasets, showing that our proposition holds in various settings.

翻译：现代神经辐射场（NeRFs）通过提议网络采样器学习从位置到体密度的映射。与使用双NeRF的粗细粒度采样方法相比，这种方法在降低网络容量方面具有显著加速潜力，因为空间坐标到体密度的映射任务不涉及视角依赖效应，因此更容易学习。鉴于大部分网络容量用于估计辐射度，NeRFs可能在其参数或深层特征中存储有价值的密度信息。为此，我们回溯一步，分析用于粗细粒度采样的大型训练ReLU-MLP。我们发现，训练后的NeRFs、Mip-NeRFs和提议网络采样器将高密度样本映射到激活特征空间中沿光线的局部极小值。我们展示了如何通过将中间激活值转换为权重估计来加速这些大型MLP，且在优化后无需对参数进行任何修改。通过我们的方法，可将训练后NeRFs的计算需求降低高达50%，仅对渲染质量造成轻微影响，且无需改变训练协议或架构。我们在多种架构和数据集上评估了该方法，表明我们的命题在不同设置下均成立。