A Pixel Is Worth More Than One 3D Gaussians in Single-View 3D Reconstruction

TL;DR

This paper introduces a hierarchical approach to single-view 3D reconstruction, representing each pixel with a parent and multiple child 3D Gaussians to better capture occluded details and improve reconstruction quality.

Contribution

It proposes a Hierarchical Splatter Image method that enhances 3D scene representation by learning multiple Gaussians per pixel, enabling better occlusion handling and detail recovery.

Findings

Achieved state-of-the-art results on ShapeNet-SRN and CO3D datasets.

Demonstrated improved occluded content reconstruction.

Outperformed existing single-image 3D reconstruction methods.

Abstract

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.