Gaussian Splatting on the Move: Blur and Rolling Shutter Compensation for Natural Camera Motion

Gaussian Splatting on the Move: Blur and Rolling Shutter Compensation for Natural Camera Motion

Introduction

The advent of differentiable rendering technologies like Neural Radiance Fields (NeRF) and Gaussian Splatting (3DGS) has significantly advanced the paradigm of novel view synthesis and scene reconstruction. These methodologies, by leveraging differentiable non-mesh-based 3D scene representations, have achieved high-fidelity visual outputs that closely rival real-world images. A fundamental caveat of these approaches, however, has been their reliance on high-quality, still photographs for accurate scene registration, making them less feasible for data captured using handheld cameras in motion. Addressing this limitation, a novel approach introduces a method to adapt 3DGS for high-quality scene reconstruction using handheld video data affected by motion blur and rolling shutter distortions.

Adaptive Gaussian Splatting for Camera Motion

To accommodate the dynamic scenarios of camera motion, this methodology incorporates an elaborate modeling of the image formation process. It utilizes velocities estimated through visual-inertial odometry (VIO) to account for camera poses that are non-static during the exposure of a single frame. This detailed modeling is facilitated by a differentiable rendering pipeline employing screen space approximation, efficiently including the effects of rolling shutter and motion blur into the 3DGS framework.

Results and Analysis

The proposed approach notably surpasses existing methods in mitigating the detrimental effects of camera motion on scene reconstruction quality. Performance evaluations conducted on both synthetic and real-world data demonstrate its superior capability in handling motion blur and rolling shutter distortions, resulting in qualitatively and quantitatively sharper reconstructions. Synthetic data experiments, based on modified Deblur-NeRF datasets inclusive of rolling shutter effects, showcased the approach’s efficacy, with significant improvements observed across all metrics (PSNR, SSIM, and LPIPS). Real-world experiments, utilizing smartphone-captured videos, further validated the method’s practical applicability and its advantage in real-life scenarios.

Theoretical Contributions and Practical Implications

The research’s theoretical contributions lie in its successful integration of detailed physical modeling of camera motion effects within the 3DGS framework, enabling the high-quality reconstruction of scenes from dynamically captured data. From a practical standpoint, this advancement opens avenues for the broader applicability of 3DGS in various real-world use cases, extending beyond the constraints of capturing still photographs to using readily available handheld video recordings. It aligns with the growing interest in rendering and reconstructing scenes from consumer-grade devices, demonstrating a significant step toward overcoming existing limitations in novel view synthesis technologies.

Future Prospects in AI and 3D Scene Reconstruction

Looking ahead, this work lays a foundation for further exploration into the integration of motion blur and rolling shutter compensation within differentiable 3D reconstruction methodologies. It points toward a future where capturing devices’ movement and inherent limitations are no longer obstacles but are effectively accounted for and corrected within the reconstruction process. This can potentially catalyze advancements in virtual and augmented reality applications, where the seamless and accurate merging of digital and physical elements is paramount. As such, the research presents a noteworthy contribution to the field of AI-driven 3D scene reconstruction, hinting at exciting possibilities for future developments.

In conclusion, the paper introduces an innovative adaptation of the Gaussian Splatting framework, enabling high-quality scene reconstruction from video data captured by motion-affected handheld cameras. By overcoming the challenges posed by motion blur and rolling shutter distortions, this approach marks a significant step forward in the usability and applicability of differentiable rendering techniques in real-world scenarios.