We introduce the Splatter Image, an ultra-fast approach for monocular 3D
object reconstruction which operates at 38 FPS. Splatter Image is based on
Gaussian Splatting, which has recently brought real-time rendering, fast
training, and excellent scaling to multi-view reconstruction. For the first
time, we apply Gaussian Splatting in a monocular reconstruction setting. Our
approach is learning-based, and, at test time, reconstruction only requires the
feed-forward evaluation of a neural network. The main innovation of Splatter
Image is the surprisingly straightforward design: it uses a 2D image-to-image
network to map the input image to one 3D Gaussian per pixel. The resulting
Gaussians thus have the form of an image, the Splatter Image. We further extend
the method to incorporate more than one image as input, which we do by adding
cross-view attention. Owning to the speed of the renderer (588 FPS), we can use
a single GPU for training while generating entire images at each iteration in
order to optimize perceptual metrics like LPIPS. On standard benchmarks, we
demonstrate not only fast reconstruction but also better results than recent
and much more expensive baselines in terms of PSNR, LPIPS, and other metrics.

我们介绍了 Splatter Image，这是一种超快的单目三维物体重建方法，运行速度达到每秒 38 帧。Splatter Image 基于高斯颗粒化技术，该技术近期实现了实时渲染、快速训练和出色的多视角重建扩展性。我们首次将高斯颗粒化应用于单目重建领域。我们的方法基于学习，测试时只需要前馈评估一个神经网络即可完成重建。Splatter Image 的主要创新之处在于其设计意外地简单：它使用一个二维图像到图像的网络将输入图像映射到每个像素点上的一个三维高斯。因此，所得到的高斯形式呈现为一张图像，即 Splatter Image。我们进一步扩展了该方法，使用多个图像作为输入，并通过添加交叉视图注意力来实现。由于渲染器的速度（每秒 588 帧），我们可以在训练时只使用一个 GPU，在每次迭代中生成整个图像，以优化 LPIPS 等感知度量标准。在标准基准测试中，我们不仅展示了快速重建，而且在 PSNR、LPIPS 和其他度量标准方面相比最近和更昂贵的基线方法取得了更好的结果。