Abstract
In this paper, we address common error sources for 3D Gaussian Splatting (3DGS) including blur, imperfect camera poses, and color inconsistencies, with the goal of improving its robustness for practical applications like reconstructions from handheld phone captures. Our main contribution involves modeling motion blur as a Gaussian distribution over camera poses, allowing us to address both camera pose refinement and motion blur correction in a unified way. Additionally, we propose mechanisms for defocus blur compensation and for addressing color in-consistencies caused by ambient light, shadows, or due to camera-related factors like varying white balancing settings. Our proposed solutions integrate in a seamless way with the 3DGS formulation while maintaining its benefits in terms of training efficiency and rendering speed. We experimentally validate our contributions on relevant benchmark datasets including Scannet++ and Deblur-NeRF, obtaining state-of-the-art results and thus consistent improvements over relevant baselines.
Overview
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Introduces a novel approach to enhance the robustness of 3D Gaussian Splatting (3DGS) against real-world imaging artifacts such as motion and defocus blur, imperfect camera poses, and color inconsistencies.
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Builds upon existing neural rendering techniques such as Neural Radiance Fields (NeRFs) and introduces improvements to address challenges commonly encountered in practical applications.
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Demonstrates significant improvements in image quality and realism through exhaustive experiments on benchmark datasets, achieving state-of-the-art results.
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Identifies future research directions, emphasizing the need for advancements in handling dynamic scenes and extending robustness to wider viewpoints.
Enhancing 3D Gaussian Splatting with Robustness to Real-World Imaging Artifacts
Introduction
The task of synthesizing photorealistic images from arbitrary camera viewpoints, known as novel view synthesis (NVS), is a crucial technology for applications ranging from augmented/virtual reality to robotics and mapping. Although recent developments like Neural Radiance Fields (NeRFs) and 3D Gaussian splats (3DGS) have pushed the boundaries of what can be achieved, these methods often assume ideal input conditions that rarely hold in practical scenarios. This paper introduces a novel approach to enhancing the robustness of 3D Gaussian Splatting (3DGS), particularly focusing on common real-world challenges such as motion and defocus blur, imperfect camera poses, and color inconsistencies.
Related Work
The field of neural rendering has rapidly evolved with methods like NeRFs demonstrating the ability to generate highly realistic images. However, these methods often falter when faced with noisy or imperfect data, an issue that real-world applications frequently encounter. Prior works have attempted to address these issues in isolation, such as NeRF in the Wild (NeRF-W) for color inconsistencies and various pose refinement techniques. Similarly, deblurring techniques in computer vision have been extensively explored, but these traditionally do not account for the complex interplay between blur, pose inaccuracies, and lighting inconsistencies when applied to neural rendering tasks.
Improvements in Robustness
This research extends the 3DGS framework to address several key issues encountered in real-world data:
- Motion Blur and Pose Errors: By modeling motion blur as a Gaussian distribution over camera poses, this work enables simultaneous correction of both camera pose inaccuracies and motion blur, seamlessly integrating this refinement within the 3DGS framework.
- Defocus Blur Compensation: An innovative mechanism is proposed for defocus blur compensation by introducing an offset correction directly within the Gaussian primitives, offering an elegant solution to depth-of-field related blurring.
- Color Inconsistencies: Addressing color inconsistencies arising from ambient light changes, camera auto-adjustments, and other factors is achieved through an RGB decoder function with per-image parameters, significantly improving color fidelity across different views.
Experimental Validation
The efficacy of the proposed enhancements is validated through exhaustive experiments on benchmark datasets like Scannet++ and Deblur-NeRF. The improvements are quantitatively demonstrated with state-of-the-art results, showcasing significant advancements over existing baselines. The experimental setup is particularly noteworthy for its real-world applicability, leveraging noisy and low-quality data typically encountered in practical scenarios.
Future Directions and Challenges
The advancements presented herein mark a significant step toward making neural rendering techniques like 3DGS more robust for real-world applications. However, challenges remain, such as handling non-static scenes and extending the robustness to viewpoints far from the training trajectory. Future research directions might explore dynamic scene representations and further enhancements to the rendering technology to address these and other emerging issues.
Conclusion
This paper addresses crucial limitations of current 3DGS implementations, proposing robust solutions to motion and defocus blur, pose inaccuracies, and color inconsistencies. By closely aligning with real-world conditions and challenges, these enhancements pave the way for broader and more practical applications of neural rendering technologies, setting a foundation for future research in the field.
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