通过形状-光线估计实现无位姿3D高斯泼溅
Pose-free 3D Gaussian splatting via shape-ray estimation
AI总结：
针对可泛化3D高斯泼溅依赖精确相机位姿、现实场景中位姿不准导致几何对齐失准的问题，提出无位姿前馈框架SHARE，通过联合形状与光线估计、位姿感知规范体表示及锚点对齐高斯预测，在多数据集上实现鲁棒的无位姿泛化渲染效果。
摘要：
尽管可泛化3D高斯泼溅能够对未见过的场景实现高效、高质量的渲染，但它严重依赖精确的相机位姿来获取准确的几何结构。在现实场景中，获取精确位姿颇具难度，会导致位姿估计存在噪声且几何对齐失准。为解决该问题，我们提出SHARE——一种无位姿的前馈高斯泼溅框架，通过联合形状与相机光线估计来消除这些模糊性。SHARE无需依赖显式3D变换，而是构建了一种具备位姿感知的规范体表示，可无缝整合多视图信息，减少由位姿估计不准确引发的对齐误差。此外，锚点对齐的高斯预测通过细化粗锚点周围的局部几何结构来增强场景重建效果，实现更精准的高斯放置。在多个真实世界数据集上开展的大量实验表明，我们的方法在无位姿可泛化高斯泼溅任务中取得了鲁棒的性能。代码可在https://github.com/youngju-na/SHARE获取。
英文摘要：
While generalizable 3D Gaussian splatting enables efficient, high-quality rendering of unseen scenes, it heavily depends on precise camera poses for accurate geometry. In real-world scenarios, obtaining accurate poses is challenging, leading to noisy pose estimates and geometric misalignments. To address this, we introduce SHARE, a pose-free, feed-forward Gaussian splatting framework that overcomes these ambiguities by joint shape and camera rays estimation. Instead of relying on explicit 3D transformations, SHARE builds a pose-aware canonical volume representation that seamlessly integrates multi-view information, reducing misalignment caused by inaccurate pose estimates. Additionally, anchor-aligned Gaussian prediction enhances scene reconstruction by refining local geometry around coarse anchors, allowing for more precise Gaussian placement. Extensive experiments on diverse real-world datasets show that our method achieves robust performance in pose-free generalizable Gaussian splatting. Code is avilable at https://github.com/youngju-na/SHARE
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。
