Hi-Gaussian: Hierarchical Gaussians under Normalized Spherical Projection for Single-View 3D Reconstruction

Binjian Xie, Pengju Zhang, Hao Wei, Yihong Wu

Keywords: Hierarchical Gaussian Sampling, Normalized Spherical Projection, Single-View 3D Reconstruction, Gaussian Splatting

TL;DR: We propose Hi-Gaussian, which employs hierarchical 3D Gaussians under normalized spherical projection for efficient and generalizable single-view 3D reconstruction.

Abstract: Single-view 3D reconstruction is a fundamental problem in computer vision, having a significant impact on downstream tasks such as Autonomous Driving, Virtual Reality and Augment Reality. However, existing single-view reconstruction methods are unable to reconstruct the regions outside the input field-of-view or the areas occluded by visible parts. In this paper, we propose Hi-Gaussian, which employs feed-forward 3D Gaussians for efficient and generalizable single-view 3D reconstruction. A Normalized Spherical Projection module is introduced following an Encoder-Decoder network in our model, assigning a larger range to the transformed spherical coordinates, which can enlarge the field of view during scene reconstruction. Besides, to reconstruct occluded regions behind the visible part, we introduce a novel Hierarchical Gaussian Sampling strategy, utilizing two layers of Gaussians to hierarchically represent 3D scenes. We first use a pre-trained monocular depth estimation model to provide depth initialization for $leader$ Gaussians, and then leverage the $leader$ Gaussians to estimate the distribution followed by $follower$ Gaussians. Extensive experiments show that our method outperforms other methods for scene reconstruction and novel view synthesis, on both outdoor and indoor datasets.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 7306