StereoGS: 基于立体先验的稀疏视角3D高斯泼溅
StereoGS: Sparse-View 3D Gaussian Splatting via Stereo Priors
AI总结：
提出StereoGS框架，利用立体深度正则化和梯度感知不透明度衰减策略，在稀疏视角下实现高质量3D重建和新视角合成，无需额外推理开销。
摘要：
3D高斯泼溅（3DGS）在实时新视角合成方面取得了显著成功，但在稀疏视角设置下由于几何约束不足而严重过拟合。虽然最近的方法引入单目深度先验来缓解这一问题，但它们固有地存在尺度模糊性和跨视角不一致性，导致几何缺陷。在本文中，我们提出了StereoGS，一种新颖的稀疏视角3DGS框架，它整合了立体先验以建立可靠的双眼一致性。与尺度无关的单目约束不同，StereoGS通过在优化过程中构建虚拟立体对并利用基础立体模型来强制绝对尺度和双眼一致结构，引入了立体深度正则化。为了进一步抑制过拟合并消除冗余基元，我们设计了一种梯度感知不透明度衰减策略，该策略根据高斯体的相对不透明度梯度幅度动态惩罚它们。结合使用零样本多视角深度估计的一致性感知密集初始化，StereoGS有效地将基元锚定到准确的场景表面。在LLFF、DTU、Mip-NeRF360和Blender数据集上的大量实验表明，StereoGS在稀疏视角设置下实现了最先进的性能，且不引入任何额外推理开销。项目页面：https://stringerywh00.github.io/StereoGS_project_page/
英文摘要：
3D Gaussian Splatting (3DGS) has achieved remarkable success in real-time novel view synthesis, yet it suffers from severe overfitting under sparse-view settings due to insufficient geometric constraints. While recent methods introduce monocular depth priors to mitigate this, they inherently struggle with scale ambiguity and cross-view inconsistency, leading to defective geometry. In this paper, we propose StereoGS, a novel sparse-view 3DGS framework that integrates stereo priors to establish reliable binocular consistency. Unlike scale-agnostic monocular constraints, StereoGS introduces a Stereo Depth Regularization by constructing virtual stereo pairs during optimization and leveraging a foundation stereo model to enforce absolute scale and binocular-consistent structures. To further suppress overfitting and eliminate redundant primitives, we design a Gradient-Aware Opacity Decay strategy that dynamically penalizes Gaussians based on their relative opacity gradient magnitudes. Combined with a Consistency-Aware Dense Initialization using zero-shot multi-view depth estimation, StereoGS effectively anchors primitives to accurate scene surfaces. Extensive experiments on LLFF, DTU, Mip-NeRF360, and Blender datasets demonstrate that StereoGS achieves state-of-the-art performance in sparse-view settings without incurring any additional inference overhead. Project Page: https://stringerywh00.github.io/StereoGS_project_page/
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。
