Real-time rendering for video games has become increasingly challenging due
to the need for higher resolutions, framerates and photorealism. Supersampling
has emerged as an effective solution to address this challenge. Our work
introduces a novel neural algorithm for supersampling rendered content that is
4 times more efficient than existing methods while maintaining the same level
of accuracy. Additionally, we introduce a new dataset which provides auxiliary
modalities such as motion vectors and depth generated using graphics rendering
features like viewport jittering and mipmap biasing at different resolutions.
We believe that this dataset fills a gap in the current dataset landscape and
can serve as a valuable resource to help measure progress in the field and
advance the state-of-the-art in super-resolution techniques for gaming content.

我们提出了一种新颖的神经算法，用于超采样渲染内容，比现有方法高效四倍，并保持相同的准确性水平。此外，我们还引入了一个新的数据集，该数据集提供了运动向量和深度等辅助模态，使用不同分辨率的图形渲染特征，如视口抖动和多级纹理偏差。我们相信，这个数据集填补了当前数据集领域的空白，并可以作为一个有价值的资源，用于帮助衡量领域进展，并推动超分辨率技术在游戏内容中的最新进展。

一种新颖游戏数据集上高效的神经超采样

Efficient neural supersampling on a novel gaming dataset

The rendering procedure used by neural radiance fields (NeRF) samples a scene
with a single ray per pixel and may therefore produce renderings that are
excessively blurred or aliased when training or testing images observe scene
content at different resolutions. The straightforward solution of supersampling
by rendering with multiple rays per pixel is impractical for NeRF, because
rendering each ray requires querying a multilayer perceptron hundreds of times.
Our solution, which we call "mip-NeRF" (a la "mipmap"), extends NeRF to
represent the scene at a continuously-valued scale. By efficiently rendering
anti-aliased conical frustums instead of rays, mip-NeRF reduces objectionable
aliasing artifacts and significantly improves NeRF's ability to represent fine
details, while also being 7% faster than NeRF and half the size. Compared to
NeRF, mip-NeRF reduces average error rates by 17% on the dataset presented with
NeRF and by 60% on a challenging multiscale variant of that dataset that we
present. Mip-NeRF is also able to match the accuracy of a brute-force
supersampled NeRF on our multiscale dataset while being 22x faster.

使用连续值比特映射扩展 NeRF, 可以高效地渲染反锯齿锥体，提高了表现精细细节的能力，缩小了占用空间，降低了平均误差率