Stereo-GS：用于通用3D高斯点云重建的多视角立体视觉模型
Stereo-GS: Multi-View Stereo Vision Model for Generalizable 3D Gaussian Splatting Reconstruction
AI总结：
Stereo-GS通过立体视觉和解耦框架实现高效通用3D高斯点云重建，无需依赖相机参数，提升鲁棒性和实用性。
摘要：
通用3D高斯点云重建展示了先进的图像到3D内容创作能力，但需要大量的计算资源和大规模数据集，这对从头训练模型提出了挑战。当前方法通常将3D高斯几何和外观的预测融为一体，这严重依赖数据驱动的先验知识，导致回归速度缓慢。为了解决这个问题，我们提出了\method，一种解耦的高效3D高斯预测框架。我们的方法通过立体视觉主干网络从局部图像对中提取特征，并通过全局注意力块进行融合。专用的点和高斯预测头生成多视角点图用于几何和高斯特征用于外观，结合成GS图来表示3DGS对象。一个细化网络增强这些GS图以实现高质量的重建。与现有方法依赖相机参数不同，我们的方法实现了无姿态的3D重建，提高了鲁棒性和实用性。通过减少资源需求同时保持高质量输出，\method为现实世界的3D内容生成提供了一种高效、可扩展的解决方案。
英文摘要：
Generalizable 3D Gaussian Splatting reconstruction showcases advanced Image-to-3D content creation but requires substantial computational resources and large datasets, posing challenges to training models from scratch. Current methods usually entangle the prediction of 3D Gaussian geometry and appearance, which rely heavily on data-driven priors and result in slow regression speeds. To address this, we propose \method, a disentangled framework for efficient 3D Gaussian prediction. Our method extracts features from local image pairs using a stereo vision backbone and fuses them via global attention blocks. Dedicated point and Gaussian prediction heads generate multi-view point-maps for geometry and Gaussian features for appearance, combined as GS-maps to represent the 3DGS object. A refinement network enhances these GS-maps for high-quality reconstruction. Unlike existing methods that depend on camera parameters, our approach achieves pose-free 3D reconstruction, improving robustness and practicality. By reducing resource demands while maintaining high-quality outputs, \method provides an efficient, scalable solution for real-world 3D content generation.
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。
