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 this https URL

提出了一种名为 Flash3D 的方法，用于从单个图像进行场景重建和新视角合成，具有很强的通用性和高效性。该方法基于基本模型进行深度估计，并使用高斯分层预测场景的 3D 形状和外观，利用高斯散点处理进行扩展。该方法训练效率高，在 RealEstate10k 数据集上取得了最先进的结果，在其他数据集上也表现出色。