  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.
