BSGS：用于相机运动去模糊的两阶段3D高斯泼溅
BSGS: Bi-stage 3D Gaussian Splatting for Camera Motion Deblurring
AI总结：
针对相机运动模糊下的3D场景重建，提出两阶段3D高斯泼溅框架，通过位姿精化和全局刚性变换校正模糊，并采用子帧梯度聚合与时空优化策略，提升重建质量，优于现有方法。
摘要：
3D高斯泼溅在3D场景重建中展现出了显著的能力。然而，从由相机运动引起的运动模糊图像中重建高质量的3D场景是一项重大挑战。现有基于3DGS的去模糊方法的性能受到其固有机制的限制，例如极度依赖相机位姿的准确性，以及无法有效控制由运动模糊引起的错误高斯原语致密化。为了解决这些问题，我们引入了一个新颖的框架——两阶段3D高斯泼溅，以从运动模糊图像中准确重建3D场景。BSGS包含两个阶段。首先，相机位姿精化粗略优化相机位姿以减少运动引起的畸变。其次，在固定的粗略相机位姿下，全局刚性变换进一步校正运动引起的模糊畸变。为了缓解多子帧梯度冲突，我们提出了一种子帧梯度聚合策略来优化这两个阶段。此外，引入了一种时空两阶段优化策略，以动态调整原语致密化阈值，并防止在模糊区域过早生成噪声高斯。全面的实验验证了我们提出的去模糊方法的有效性，并展示了其相对于最先进方法的优越性。我们的源代码可在 https://github.com/wsxujm/bsgs 获取。
英文摘要：
3D Gaussian Splatting has exhibited remarkable capabilities in 3D scene reconstruction. However, reconstructing high-quality 3D scenes from motion-blurred images caused by camera motion poses a significant challenge.The performance of existing 3DGS-based deblurring methods are limited due to their inherent mechanisms, such as extreme dependence on the accuracy of camera poses and inability to effectively control erroneous Gaussian primitives densification caused by motion blur. To solve these problems, we introduce a novel framework, Bi-Stage 3D Gaussian Splatting, to accurately reconstruct 3D scenes from motion-blurred images. BSGS contains two stages. First, Camera Pose Refinement roughly optimizes camera poses to reduce motion-induced distortions. Second, with fixed rough camera poses, Global RigidTransformation further corrects motion-induced blur distortions. To alleviate multi-subframe gradient conflicts, we propose a subframe gradient aggregation strategy to optimize both stages. Furthermore, a space-time bi-stage optimization strategy is introduced to dynamically adjust primitive densification thresholds and prevent premature noisy Gaussian generation in blurred regions. Comprehensive experiments verify the effectiveness of our proposed deblurring method and show its superiority over the state of the arts.Our source code is available at https://github.com/wsxujm/bsgs
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
