This paper tackles the intricate challenge of object removal to update the
radiance field using the 3D Gaussian Splatting. The main challenges of this
task lie in the preservation of geometric consistency and the maintenance of
texture coherence in the presence of the substantial discrete nature of
Gaussian primitives. We introduce a robust framework specifically designed to
overcome these obstacles. The key insight of our approach is the enhancement of
information exchange among visible and invisible areas, facilitating content
restoration in terms of both geometry and texture. Our methodology begins with
optimizing the positioning of Gaussian primitives to improve geometric
consistency across both removed and visible areas, guided by an online
registration process informed by monocular depth estimation. Following this, we
employ a novel feature propagation mechanism to bolster texture coherence,
leveraging a cross-attention design that bridges sampling Gaussians from both
uncertain and certain areas. This innovative approach significantly refines the
texture coherence within the final radiance field. Extensive experiments
validate that our method not only elevates the quality of novel view synthesis
for scenes undergoing object removal but also showcases notable efficiency
gains in training and rendering speeds.

通过使用三维高斯图案生成器更新辐射场，本研究论文解决了对象去除的复杂挑战，主要挑战在于在高斯图案的离散性质中保持几何一致性和纹理协调性。通过优化高斯图案的定位以提高去除区域和可见区域的几何一致性，再结合跨注意力设计的新颖特征传递机制来增强纹理协调性，该创新方法在最终辐射场中显著提高了纹理协调性。广泛的实验证实了我们的方法不仅提升了对象去除场景的新视图合成质量，而且在训练和渲染速度上也具有显著的效率提升。