Efficient 3D Gaussian Splatting for High-Quality Object Removal from Radiance Fields

Основные понятия

A novel framework, GScream, that leverages 3D Gaussian Splatting to efficiently and effectively remove objects from radiance fields while maintaining geometric consistency and texture coherence.

Аннотация

The paper introduces GScream, a framework that utilizes 3D Gaussian Splatting (3DGS) to tackle the challenge of object removal from radiance fields. The key insights are:

Geometry Consistency: To address the geometric inconsistency in the removal area, the method incorporates monocular depth estimation as an extra constraint to optimize the positioning of Gaussian primitives, enhancing the geometric consistency across both removed and visible areas.

Texture Coherence: To ensure coherent texture synthesis in the in-painted region, the method exploits the explicit representation capability of 3DGS. It proposes a novel feature regularization strategy that facilitates improved interaction between Gaussian clusters in both the in-painted and visible sections of the scene, propagating texture information from the visible to the in-painted regions.

Efficiency: The method adopts a lightweight Gaussian Splatting architecture, Scaffold-GS, to mitigate the computational burden associated with directly manipulating millions of Gaussians, significantly enhancing the efficiency and effectiveness of the rendering process.

Extensive experiments validate that the proposed GScream 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 compared to traditional NeRF-based methods.

Статистика

The paper reports the following key metrics:

PSNR: 20.49
Masked PSNR: 15.84
SSIM: 0.58
Masked SSIM: 0.21
LPIPS: 0.28
Masked LPIPS: 0.54
FID: 36.72
Training Time: ~1.2 hours

Цитаты

"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."
"We introduce a novel feature propagation mechanism to bolster texture coherence, leveraging a cross-attention design that bridges sampling Gaussians from both uncertain and certain areas."

Ключевые выводы из

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

by Yuxin Wang,Q... в arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13679.pdf

Дополнительные вопросы

How can the proposed GScream framework be extended to handle dynamic scenes or scenes with multiple removable objects

The GScream framework can be extended to handle dynamic scenes or scenes with multiple removable objects by incorporating temporal information and object segmentation techniques.

Temporal Information: To handle dynamic scenes, the framework can be modified to incorporate information from consecutive frames. By leveraging temporal coherence, the system can track object movements and changes over time, allowing for more accurate object removal and scene completion.

Object Segmentation: For scenes with multiple removable objects, advanced object segmentation algorithms can be integrated into the framework. By accurately identifying and segmenting each object in the scene, the system can apply the object removal process individually to each object, ensuring a comprehensive and precise removal result.

Multi-Object Handling: The framework can be enhanced to support the removal of multiple objects simultaneously. By extending the feature propagation mechanism to handle interactions between multiple objects and their surrounding areas, the system can maintain consistency and coherence in the rendered output.

By incorporating these enhancements, the GScream framework can effectively handle dynamic scenes and scenes with multiple removable objects, providing robust and accurate object removal capabilities.

What are the potential limitations of the 3D Gaussian Splatting representation, and how can they be addressed to further improve the object removal task

The 3D Gaussian Splatting representation, while effective for object removal tasks, has some potential limitations that can be addressed to further improve the task:

Discretization Artifacts: The discrete nature of Gaussian primitives can lead to inaccuracies in representing complex geometry. To address this, advanced sampling techniques or adaptive refinement strategies can be implemented to enhance the representation of intricate geometric details.

Texture Incoherence: Filling regions behind removed objects with consistent textures can be challenging. To improve texture coherence, additional texture synthesis algorithms or texture blending methods can be integrated into the framework to ensure smooth transitions and realistic textures in the rendered output.

Efficiency: While Gaussian Splatting offers real-time rendering capabilities, further optimizations can be made to improve efficiency. This can include parallel processing techniques, GPU acceleration, or model compression methods to enhance training and rendering speeds without compromising quality.

By addressing these limitations, the 3D Gaussian Splatting representation can be refined to provide more accurate and visually appealing object removal results.

Can the cross-attention feature regularization mechanism be applied to other 3D representation methods beyond Gaussian Splatting to enhance texture coherence in object removal

The cross-attention feature regularization mechanism can be applied to other 3D representation methods beyond Gaussian Splatting to enhance texture coherence in object removal tasks.

Volumetric Representations: For volumetric representations like voxel grids or implicit functions, the cross-attention mechanism can facilitate information exchange between neighboring voxels or points. By incorporating feature regularization through cross-attention, these representations can improve texture consistency and coherence in object removal scenarios.

Point Clouds: In point cloud representations, the cross-attention mechanism can enable feature interactions between points in the cloud. By leveraging cross-attention for feature propagation, point cloud representations can enhance texture synthesis and maintain consistency in object removal processes.

Mesh Representations: For mesh-based representations, the cross-attention mechanism can be utilized to propagate features between mesh vertices. This can help refine texture details and ensure smooth transitions between removed object regions and their surroundings, enhancing the overall visual quality of the rendered output.

By applying the cross-attention feature regularization mechanism to various 3D representation methods, texture coherence in object removal tasks can be significantly improved, leading to more realistic and visually appealing results.