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Quadratic Gaussian Splatting: High Quality Surface Reconstruction with Second-order Geometric Primitives

About

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.

Ziyu Zhang, Binbin Huang, Hanqing Jiang, Liyang Zhou, Xiaojun Xiang, Shunhan Shen• 2024

Related benchmarks

TaskDatasetResultRank
Novel View SynthesisNeRF Synthetic
PSNR30.41
110
Surface ReconstructionTanks&Temples
Mean50
57
Surface ReconstructionDTU
Scan 24 Metric Value0.38
34
Surface ReconstructionDTU (test)
DTU Metric 240.38
22
Novel View SynthesisMip-NeRF 360 Indoor Scenes
PSNR30.45
21
Novel View SynthesisMip-NeRF 360 Outdoor Scenes
PSNR24.32
21
3D ReconstructionTanks and Temples (TNT) (test)
F1-score (Barn)55
18
Surface ReconstructionNeRF Synthetic--
11
Geometry ReconstructionDTU
Chamfer Distance (Scan 24)0.45
10
3D ReconstructionScanNet++ v2
Scale Accuracy64.6
6
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