Reconstructing and rendering 3D objects from highly sparse views is of
critical importance for promoting applications of 3D vision techniques and
improving user experience. However, images from sparse views only contain very
limited 3D information, leading to two significant challenges: 1) Difficulty in
building multi-view consistency as images for matching are too few; 2)
Partially omitted or highly compressed object information as view coverage is
insufficient. To tackle these challenges, we propose GaussianObject, a
framework to represent and render the 3D object with Gaussian splatting, that
achieves high rendering quality with only 4 input images. We first introduce
techniques of visual hull and floater elimination which explicitly inject
structure priors into the initial optimization process for helping build
multi-view consistency, yielding a coarse 3D Gaussian representation. Then we
construct a Gaussian repair model based on diffusion models to supplement the
omitted object information, where Gaussians are further refined. We design a
self-generating strategy to obtain image pairs for training the repair model.
Our GaussianObject is evaluated on several challenging datasets, including
MipNeRF360, OmniObject3D, and OpenIllumination, achieving strong reconstruction
results from only 4 views and significantly outperforming previous
state-of-the-art methods.

从稀疏视图中重建和渲染 3D 物体是促进 3D 视觉技术应用和提高用户体验的关键。本文提出了 GaussianObject 框架，通过高斯散射达到高质量渲染，仅需 4 张输入图像，解决了构建多视角一致性和补充遗漏物体信息的困难，同时在多个数据集上显示出卓越的重建效果，超越现有方法。