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.

翻译：我们提出Splatter Image方法，这是一种基于单目图像的超高速三维物体重建方案，运行帧率可达38 FPS。Splatter Image以高斯泼溅技术为基础，该技术近期已在多视图重建中实现了实时渲染、快速训练和卓越的可扩展性。我们首次将高斯泼溅应用于单目重建场景。该方法基于学习范式，测试时仅需通过神经网络的前向传播即可完成重建。Splatter Image的核心创新在于其惊人的简洁设计：采用二维图像到图像的映射网络，将输入图像逐像素转化为三维高斯体。由此生成的高斯体呈现为图像形式，即Splatter Image。我们进一步扩展该方法以支持多幅图像输入，通过引入跨视角注意力机制实现。得益于渲染器的高效性能（588 FPS），单张GPU即可完成训练，同时每次迭代生成完整图像以优化LPIPS等感知指标。在标准基准测试中，该方法不仅在PSNR、LPIPS等指标上优于近期更昂贵的基线方法，更展现出极快的重建速度。