  In this paper, we propose Flash3D, a method for scene reconstruction and
novel view synthesis from a single image which is both very generalisable and
efficient. For generalisability, we start from a "foundation" model for
monocular depth estimation and extend it to a full 3D shape and appearance
reconstructor. For efficiency, we base this extension on feed-forward Gaussian
Splatting. Specifically, we predict a first layer of 3D Gaussians at the
predicted depth, and then add additional layers of Gaussians that are offset in
space, allowing the model to complete the reconstruction behind occlusions and
truncations. Flash3D is very efficient, trainable on a single GPU in a day, and
thus accessible to most researchers. It achieves state-of-the-art results when
trained and tested on RealEstate10k. When transferred to unseen datasets like
NYU it outperforms competitors by a large margin. More impressively, when
transferred to KITTI, Flash3D achieves better PSNR than methods trained
specifically on that dataset. In some instances, it even outperforms recent
methods that use multiple views as input. Code, models, demo, and more results
are available at https://www.robots.ox.ac.uk/~vgg/research/flash3d/.
