用可变形二次曲面提升三维重建精度,更真实还原物体表面形状。
Quadratic Gaussian Splatting: High Quality Surface Reconstruction with Second-order Geometric Primitives
- 用二次曲面替代平面点元,通过测地距离自适应调整密度分布。
- 在DTU数据集上几何误差比2DGS降低33%,比GOF降低27%。
- 适合需要高精度几何的机器人导航与沉浸式虚拟现实应用。
我们提出二次高斯点云(QGS),一种新表征方式,将静态点元替换为可变形的二次曲面(如椭圆、抛物面),以捕捉复杂几何结构。与以往依赖欧氏距离建模点元密度的方法不同——该度量在形变下与表面几何不匹配——QGS引入基于测地距离的密度分布,使密度权重能内在适应曲面曲率,确保形状变化(如从平面盘到弯曲抛物面)过程中的一致性。通过在二次曲面上闭式求解测地距离,QGS实现面向表面的点云渲染,单个原语即可表示此前需数十个平面点元才能表达的复杂曲率,显著降低内存占用,并通过快速射线-二次曲面相交实现高效渲染。在DTU、Tanks and Temples和MipNeRF360数据集上的实验表明,QGS达到最先进的表面重建效果,在DTU数据集上几何误差(切比雪夫距离)相比2DGS降低33%,相比GOF降低27%。关键的是,QGS保持了良好的外观质量,弥合了几何精度与视觉保真度之间的差距,适用于机器人与沉浸式现实等场景。
原文摘要 · Abstract (English)
We propose Quadratic Gaussian Splatting (QGS), a novel representation that replaces static primitives with deformable quadric surfaces (e.g., ellipse, paraboloids) to capture intricate geometry. Unlike prior works that rely on Euclidean distance for primitive density modeling--a metric misaligned with surface geometry under deformation--QGS introduces geodesic distance-based density distributions. This innovation ensures that density weights adapt intrinsically to the primitive curvature, preserving consistency during shape changes (e.g., from planar disks to curved paraboloids). By solving geodesic distances in closed form on quadric surfaces, QGS enables surface-aware splatting, where a single primitive can represent complex curvature that previously required dozens of planar surfels, potentially reducing memory usage while maintaining efficient rendering via fast ray-quadric intersection. Experiments on DTU, Tanks and Temples, and MipNeRF360 datasets demonstrate state-of-the-art surface reconstruction, with QGS reducing geometric error (chamfer distance) by 33% over 2DGS and 27% over GOF on the DTU dataset. Crucially, QGS retains competitive appearance quality, bridging the gap between geometric precision and visual fidelity for applications like robotics and immersive reality.
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