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“飞溅图像”(Splatter Image)，这是一种超快速的单目三维物体重建方法，工作速度可达每秒38帧。飞溅图像基于高斯飞溅(Gaussian Splatting)技术，该技术最近在多视角重建领域带来了实时渲染、快速训练和优秀的扩展能力。这是我们首次将高斯飞溅应用于单目重建环境。我们的方法基于学习，且在测试时，重建仅需要神经网络的前向评估。飞溅图像的主要创新在于其出人意料的简洁设计：它使用二维图像到图像的网络将输入图像映射到每个像素的一个三维高斯上。因此，生成的高斯以图像的形式呈现，即飞溅图像。我们进一步扩展了该方法，以纳入多个图像作为输入，这是通过添加交叉视图注意力实现的。由于渲染器的高速（每秒588帧），我们可以在单个GPU上进行训练，同时在每次迭代中生成整个图像，以优化感知度量，如LPIPS。在标准基准测试中，我们不仅展示了快速的重建速度，还在PSNR、LPIPS和其他指标上展示了比近期更昂贵的基线方法更好的结果。