OC-GS：针对不规则转盘拍摄的高斯泼溅重建方法
OC-GS: Gaussian Splatting for Irregular Turntable Capture
转盘拍物体角度不均匀、帧还丢时，普通重建方法容易崩。这篇 OC-GS 靠逐张细化角度，6 个视角也能重建，效果比四个基线都好。
OC-GS 是一种面向物体的高斯泼溅方法，专为转盘拍摄中旋转不均匀、丢帧导致的等角度假设失效问题设计。它在共享相机、旋转轴和支点的前提下，逐张图像细化拍摄角度，联合优化几何与角度。在使用 12、8、6 个不规则视角的渲染测试中，前景 PSNR 分别达到 21.26、19.36、15.83dB，超过全部四个无位姿高斯泼溅基线。相比固定角度估计，细化图像角度使前景 PSNR 平均提升 7.88dB，真实拍摄数据上提升 0.70dB。
OC-GS: Gaussian Splatting for Irregular Turntable Capture
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.