论文标题
一个是：弥合神经辐射场之间的缝隙，并进行渐进的体积蒸馏
One is All: Bridging the Gap Between Neural Radiance Fields Architectures with Progressive Volume Distillation
论文作者
论文摘要
Neural Radiance Fields (NeRF) methods have proved effective as compact, high-quality and versatile representations for 3D scenes, and enable downstream tasks such as editing, retrieval, navigation, etc. Various neural architectures are vying for the core structure of NeRF, including the plain Multi-Layer Perceptron (MLP), sparse tensors, low-rank tensors, hashtables and their组成。这些表示中的每一个都有其特定的权衡。例如，基于标签的表示可以接受更快的培训和渲染速度，但缺乏明确的几何形状，这意味着hampers hampers下游任务（如空间关系 - 意识到的编辑）。在本文中，我们提出了一种渐进式体积蒸馏（PVD），这是一种系统的蒸馏方法，允许在包括MLP，稀疏或低级别张量，散布物及其组成的不同体系结构之间进行任何转换。因此，PVD赋予下游应用程序，以以事后方式最佳地适应手头的神经表示。转换很快，因为从较浅到更深层次的不同级别的体积表示上进行了蒸馏。我们还对密度进行特殊处理来处理其特定的数值不稳定性问题。提供了经验证据，以验证我们对NERF合成，LLFF和Tanksandtplass数据集的方法。例如，使用PVD，基于MLP的NERF模型可以从基于黑标的NGP模型中蒸馏出10x〜20X的速度，而不是从SCRATCH中训练原始的NERF，同时获得了较高的合成质量。代码可在https://github.com/megvii-research/aaai2023-pvd上找到。
Neural Radiance Fields (NeRF) methods have proved effective as compact, high-quality and versatile representations for 3D scenes, and enable downstream tasks such as editing, retrieval, navigation, etc. Various neural architectures are vying for the core structure of NeRF, including the plain Multi-Layer Perceptron (MLP), sparse tensors, low-rank tensors, hashtables and their compositions. Each of these representations has its particular set of trade-offs. For example, the hashtable-based representations admit faster training and rendering but their lack of clear geometric meaning hampers downstream tasks like spatial-relation-aware editing. In this paper, we propose Progressive Volume Distillation (PVD), a systematic distillation method that allows any-to-any conversions between different architectures, including MLP, sparse or low-rank tensors, hashtables and their compositions. PVD consequently empowers downstream applications to optimally adapt the neural representations for the task at hand in a post hoc fashion. The conversions are fast, as distillation is progressively performed on different levels of volume representations, from shallower to deeper. We also employ special treatment of density to deal with its specific numerical instability problem. Empirical evidence is presented to validate our method on the NeRF-Synthetic, LLFF and TanksAndTemples datasets. For example, with PVD, an MLP-based NeRF model can be distilled from a hashtable-based Instant-NGP model at a 10X~20X faster speed than being trained the original NeRF from scratch, while achieving a superior level of synthesis quality. Code is available at https://github.com/megvii-research/AAAI2023-PVD.
