Quadratic Gaussian Splatting: High Quality Surface Reconstruction with Second-Order Geometric Primitives
Ziyu Zhang, Binbin Huang, Hanqing Jiang, Liyang Zhou, Xiaojun Xiang, Shuhan Shen
摘要
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 surfaceaware 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 2 DGS 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.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper8
- Radiance Meshes for Volumetric ReconstructionAlexander Mai, Trevor Hedstrom, George Kopanas, Janne Kontkanen 等CVPR 2026 · 被引用 8 次
- PlanarGS: High-Fidelity Indoor 3D Gaussian Splatting Guided by Vision-Language Planar PriorsXirui Jin, Renbiao Jin, Boying Li, Danping Zou 等NeurIPS 2025 · 被引用 5 次
- Universal Beta SplattingRong Liu, Zhongpai Gao, Benjamin Planche, Meida Chen 等ICLR 2026 · 被引用 4 次
- ExMesh: EXplicit Mesh Reconstruction with Topology AdaptationChuanjin Fan, Lifan Wu, Wenjie Chang, Hanzhi Chang 等CVPR 2026 · 被引用 2 次
- 3D Gaussian Splatting with Self-Constrained Priors for High Fidelity Surface ReconstructionTakeshi Noda, Yu-Shen Liu, Zhizhong HanCVPR 2026 · 被引用 2 次
它引用的顶会 Paper27
- 3D Gaussian Splatting for Real-Time Radiance Field RenderingBernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George DrettakisSIGGRAPH 2023 · 被引用 5,687 次
- Instant neural graphics primitives with a multiresolution hash encodingThomas Müller, Alex Evans, Christoph Schied, Alexander KellerSIGGRAPH 2022 · 被引用 4,089 次
- Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance FieldsJonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman 等ICCV 2021 · 被引用 2,700 次
- NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view ReconstructionPeng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt 等NeurIPS 2021 · 被引用 2,500 次
- Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance FieldsJonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan 等CVPR 2022 · 被引用 1,603 次
相关 Paper
- Tetrahedron Splatting for 3D GenerationChun Gu, Zeyu Yang, Zijie Pan, Xiatian Zhu 等NeurIPS 2024 · 被引用 13 次
- BillBoard Splatting (BBSplat): Learnable Textured Primitives for Novel View SynthesisDavid Svitov, Pietro Morerio, Lourdes Agapito, Alessio Del BueICCV 2025 · 被引用 7 次
- Prune Wisely, Reconstruct Sharply: Compact 3D Gaussian Splatting via Adaptive Pruning and Difference-of-Gaussian PrimitivesHaoran Wang, Guoxi Huang, Fan Zhang, David Bull 等CVPR 2026 · 被引用 6 次
- Path Matters: Unveiling Geometric Implicit Bias via Curvature-Aware Sparse View OptimizationCanran Xiao, Liaoyuan Fan, Yanbin Li, Jing Tang 等ICLR 2026
- GSRecon: Efficient Generalizable Gaussian Splatting for Surface Reconstruction from Sparse ViewsHang Yang, Le Hui, Jianjun Qian, Jin Xie 等ICCV 2025 · 被引用 1 次
