Learning 3D scene representation from a single-view image is a long-standing fundamental problem in computer vision, with the inherent ambiguity in predicting contents unseen from the input view. Built on the recently proposed 3D Gaussian Splatting (3DGS), the Splatter Image method has made promising progress on fast single-image novel view synthesis via learning a single 3D Gaussian for each pixel based on the U-Net feature map of an input image. However, it has limited expressive power to represent occluded components that are not observable in the input view. To address this problem, this paper presents a Hierarchical Splatter Image method in which a pixel is worth more than one 3D Gaussians. Specifically, each pixel is represented by a parent 3D Gaussian and a small number of child 3D Gaussians. Parent 3D Gaussians are learned as done in the vanilla Splatter Image. Child 3D Gaussians are learned via a lightweight Multi-Layer Perceptron (MLP) which takes as input the projected image features of a parent 3D Gaussian and the embedding of a target camera view. Both parent and child 3D Gaussians are learned end-to-end in a stage-wise way. The joint condition of input image features from eyes of the parent Gaussians and the target camera position facilitates learning to allocate child Gaussians to ``see the unseen'', recovering the occluded details that are often missed by parent Gaussians. In experiments, the proposed method is tested on the ShapeNet-SRN and CO3D datasets with state-of-the-art performance obtained, especially showing promising capabilities of reconstructing occluded contents in the input view.

翻译：从单视图图像学习三维场景表示是计算机视觉中长期存在的基础性问题，其核心挑战在于预测输入视角不可见内容时固有的模糊性。基于近期提出的三维高斯溅射（3DGS）技术，Splatter Image方法通过基于输入图像的U-Net特征图为每个像素学习单个三维高斯，在快速单图像新视角合成方面取得了显著进展。然而，该方法在表示输入视角不可见的遮挡部件时表达能力有限。为解决此问题，本文提出一种层次化Splatter Image方法，其中单个像素对应多个三维高斯。具体而言，每个像素由一个父三维高斯和若干子三维高斯共同表示。父三维高斯沿用原始Splatter Image方法进行学习，而子三维高斯则通过轻量级多层感知机（MLP）进行学习——该网络以父三维高斯的投影图像特征和目标相机视角的嵌入向量作为输入。父高斯与子高斯通过分阶段方式进行端到端联合学习。这种结合父高斯视角图像特征与目标相机位置的联合条件机制，能够促进子高斯学习如何“看见不可见内容”，有效恢复常被父高斯遗漏的遮挡细节。在ShapeNet-SRN和CO3D数据集上的实验表明，所提方法取得了最先进的性能，尤其在重建输入视角中遮挡内容方面展现出显著优势。