With the advent of Neural Radiance Fields (NeRF), neural networks can now
render novel views of a 3D scene with quality that fools the human eye. Yet,
generating these images is very computationally intensive, limiting their
applicability in practical scenarios. In this paper, we propose a technique
based on spatial decomposition capable of mitigating this issue. Our key
observation is that there are diminishing returns in employing larger (deeper
and/or wider) networks. Hence, we propose to spatially decompose a scene and
dedicate smaller networks for each decomposed part. When working together,
these networks can render the whole scene. This allows us near-constant
inference time regardless of the number of decomposed parts. Moreover, we show
that a Voronoi spatial decomposition is preferable for this purpose, as it is
provably compatible with the Painter's Algorithm for efficient and GPU-friendly
rendering. Our experiments show that for real-world scenes, our method provides
up to 3x more efficient inference than NeRF (with the same rendering quality),
or an improvement of up to 1.0~dB in PSNR (for the same inference cost).

本文介绍一种基于空间分解的神经辐射场渲染技术，实验表明该技术在保证渲染质量的同时，提供了比 Neural Radiance Fields 更高效的推理速度，可 GPU 友好地进行渲染。

DeRF: 分解辐射场

DeRF: Decomposed Radiance Fields

We present a new method for efficient high-quality image segmentation of
objects and scenes. By analogizing classical computer graphics methods for
efficient rendering with over- and undersampling challenges faced in pixel
labeling tasks, we develop a unique perspective of image segmentation as a
rendering problem. From this vantage, we present the PointRend (Point-based
Rendering) neural network module: a module that performs point-based
segmentation predictions at adaptively selected locations based on an iterative
subdivision algorithm. PointRend can be flexibly applied to both instance and
semantic segmentation tasks by building on top of existing state-of-the-art
models. While many concrete implementations of the general idea are possible,
we show that a simple design already achieves excellent results. Qualitatively,
PointRend outputs crisp object boundaries in regions that are over-smoothed by
previous methods. Quantitatively, PointRend yields significant gains on COCO
and Cityscapes, for both instance and semantic segmentation. PointRend's
efficiency enables output resolutions that are otherwise impractical in terms
of memory or computation compared to existing approaches. Code has been made
available at
this https URL

本文提出了一种新的高质量图像分割方法，其中通过将传统的计算机图形学方法与像素标注任务中所面临的过采样和欠采样挑战相类比，开发了将图像分割视为渲染问题的独特视角，并基于迭代细分算法在自适应选择的位置执行基于点的分割预测的 PointRend（基于点的渲染）神经网络模块，该模块可以在现有的最先进模型的基础上灵活地应用于实例和语义分割任务上。