无需精确相机位姿即可实现高质量3D高斯点云渲染
Pose-free 3D Gaussian splatting via shape-ray estimation
- 通过联合估计形状与相机光线，构建无位姿依赖的统一表示
- 在真实场景数据集上实现稳定重建，几何误差显著降低
- 适合缺乏准确位姿信息的实时三维重建应用
通用3D高斯点云渲染虽能高效高质量呈现未知场景，但严重依赖精确的相机位姿。在真实场景中，获取精确位姿困难，导致位姿噪声和几何错配。为此，我们提出SHARE——一种无位姿、前馈式的高斯点云渲染框架，通过联合估计形状与相机光线，克服此类模糊性。不同于显式三维变换，SHARE构建了具有位姿感知能力的规范体素表示，无缝融合多视角信息，减少因位姿不准引起的错配。此外，锚点对齐的高斯预测通过精修粗锚点附近的局部几何，实现更精准的高斯分布。在多种真实世界数据集上的大量实验表明，该方法在无位姿泛化高斯点云渲染中表现稳健。代码已开源：https://github.com/youngju-na/SHARE
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。