GScream: Learning 3D Geometry and Feature Consistent Gaussian Splatting for Object Removal

Abstract: 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.

• The paper presents GScream, a framework that improves object removal by integrating depth-guided Gaussian optimization for enhanced geometric consistency.
• It employs a novel cross-attention mechanism to ensure seamless texture coherence between inpainted and visible regions.
• Experimental results demonstrate significant gains in rendering efficiency and visual fidelity compared to traditional NeRF-based methods.

Enhancements in 3D Object Removal via Gaussian Splatting with Emphasis on Geometric and Textural Consistency

Introduction to 3DGS in Object Removal

The paper introduces a strategic advancement in the domain of 3D object removal from pre-captured scenes by employing a method based on 3D Gaussian Splatting (3DGS). The approach mainly addresses the challenges of achieving geometric and textural consistency post-object removal, which are critical for maintaining the realism in synthesized views. This involves optimizing Gaussian placements with depth guidance and enhancing texture coherence through a novel feature interaction mechanism.

Methodology and Approach

Overview of the GScream Framework

The proposed system, termed GScream, leverages 3DGS, providing a structured approach that significantly improves upon the weaknesses of existing NeRF-based methods, particularly in rendering speeds and the handling of discrete Gaussian elements. The core contributions of this method are:

1. Geometric Consistency and Accuracy: Incorporating multi-view monocular depth estimates substantially enhances the positioning and alignment of Gaussians, thus accurately modeling the scene geometry post object removal.
2. Textural Coherence: A cross-attention feature interaction mechanism is introduced to align the textures between the visible and newly synthesized regions, ensuring coherent and high-fidelity textural output across varied viewing angles.

Detailed Methodological Innovations

1. Depth Guided Gaussian Optimization: By aligning the 3D Gaussian placement with depth estimations from multiple views, the approach refines the geometric base which is crucial for subsequent textural propagation.
2. Feature Regularization via Cross-Attention: In a novel use of 3DGS's explicit representation capabilities, the GScream framework utilizes a cross-attention mechanism to enhance feature compatibility between adjacent Gaussians across the in-painted and visible regions.

Experimental Evaluation

Performance Metrics and Comparison

The GScream model showcases superior performance in rendering speed and visual quality compared to traditional NeRF implementations:

• Efficiency Gains: GScream reports significant reductions in training and rendering time, with a quantifiable decrease in computational overhead due to the lightweight architecture of Scaffold-GS.
• Visual Quality: Across multiple standard metrics like PSNR, SSIM, and FID, GScream consistently performs better than or on par with contemporary methods, suggesting better consistency and quality in the texture and geometry of rendered scenes.

Qualitative and Quantitative Analyses

The experimental results underline:

• Improved handling of complex scenes with more realistic texture filling and seamless transitions in the in-painted regions.
• Notable improvements in object removal outcomes with GScream, particularly in scenes that demand high fidelity in texture continuity and geometric plausibility.

Theoretical and Practical Implications

The success of GScream in addressing both geometric and textural consistency for object removal opens significant avenues in virtual reality and content generation applications. From a theoretical perspective, this work enhances the understanding of leveraging explicit 3D representations for complex scene manipulations. Practically, the method sets a precedent for efficiently handling large-scale 3D data with intricate textural and geometric details.

Future Directions in AI and 3D Modeling

Looking ahead, the potential for integrating more dynamic object models and real-time interaction systems in 3D scenes is vast. Further research could explore more sophisticated depth estimation techniques and real-time feedback mechanisms to refine this framework for live 3D content creation and manipulation, potentially expanding its applicability to interactive gaming and real-time simulation scenarios. Additionally, extending this framework to handle more complex object interactions and multiple object removals concurrently could significantly impact the field of 3D scene synthesis and editing.