OC-GS：面向不规则转台拍摄的高斯泼溅
OC-GS: Gaussian Splatting for Irregular Turntable Capture
- Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对转台拍摄中旋转不均与丢帧导致等角假设失效的问题，提出OC-GS，通过轨道一致的细化联合优化图像几何与角度，在共享运动模型下提升稀疏不规则拍摄的重建质量，超越多个无姿态基线。
AI中文摘要：
不均匀的旋转和丢帧使得等角假设在转台重建中变得不可靠。我们提出OC-GS，一种以物体为中心的高斯泼溅方法，它在保持共享相机、旋转轴和枢轴的同时，细化每张图像的角度。这种轨道一致的细化方法联合优化了从图像导出的几何和角度，以从稀疏、不规则的拍摄中重建物体。在具有12、8和6个不规则间隔视图的渲染物体上，OC-GS分别实现了21.26、19.36和15.83dB的平均前景PSNR，在每种条件下均超过了所有四个评估的无姿态高斯泼溅基线。在共享训练器下，细化图像估计的角度相比保持这些估计固定，将平均前景PSNR提高了7.88dB。消融研究表明，图像导出的角度初始化和共享运动模型都对改进有贡献。在真实拍摄中，OC-GS的细化使平均前景PSNR提高了0.70dB。结果表明，在共享运动模型内细化不确定角度，可以改进从稀疏、不规则转台拍摄的重建。
英文摘要：
Uneven rotation and dropped frames make equal-angle assumptions unreliable for turntable reconstruction. We present OC-GS, an object-centric Gaussian splatting that refines each image's angle while maintaining a shared camera, rotation axis, and pivot. This orbit-consistent refinement jointly optimizes image-derived geometry and angles to reconstruct objects from sparse, irregular captures. On rendered objects with 12, 8, and 6 irregularly spaced views, OC-GS achieves mean foreground PSNR scores of 21.26, 19.36, and 15.83dB, respectively, exceeding all four evaluated pose-free Gaussian splatting baselines in each condition. Under a shared trainer, refining image-estimated angles improves mean foreground PSNR by 7.88dB over keeping those estimates fixed. An ablation study shows that both image-derived angle initialization and the shared motion model contribute to the improvement. On real captures, OC-GS's refinement increases mean foreground PSNR by 0.70dB. Results show that refining uncertain angles within a shared motion model improves reconstruction from sparse, irregular turntable captures.
