  We introduce the \method, an ultra-efficient approach for monocular 3D object
reconstruction. Splatter Image is based on Gaussian Splatting, which allows
fast and high-quality reconstruction of 3D scenes from multiple images. We
apply Gaussian Splatting to monocular reconstruction by learning a neural
network that, at test time, performs reconstruction in a feed-forward manner,
at 38 FPS. Our main innovation is the surprisingly straightforward design of
this network, which, using 2D operators, maps the input image to one 3D
Gaussian per pixel. The resulting set of Gaussians thus has the form an image,
the Splatter Image. We further extend the method take several images as input
via cross-view attention. Owning to the speed of the renderer (588 FPS), we use
a single GPU for training while generating entire images at each iteration to
optimize perceptual metrics like LPIPS. On several synthetic, real,
multi-category and large-scale benchmark datasets, we achieve better results in
terms of PSNR, LPIPS, and other metrics while training and evaluating much
faster than prior works. Code, models, demo and more results are available at
https://szymanowiczs.github.io/splatter-image.
