studentSplat: 你的学生模型学习单视图3D高斯散射
studentSplat: Your Student Model Learns Single-view 3D Gaussian Splatting
AI总结：
studentSplat通过教师-学生架构和外推网络，实现单视图3D场景重建，达到最先进的单视图重建质量并具备自监督深度估计能力。
摘要：
近年来，前馈3D高斯散射的进步使多视图3D场景重建或单视图3D物体重建取得了显著成果，但单视图3D场景重建仍因单视图中的固有歧义而受到限制。我们提出了studentSplat，一种用于场景重建的单视图3D高斯散射方法。为克服单一输入中固有视图扩展问题带来的尺度歧义和外推问题，我们引入了两种技术：1）一种教师-学生架构，其中多视图教师模型在训练过程中为单视图学生提供几何监督，解决尺度歧义并鼓励几何有效性；2）一个外推网络，完成缺失的场景上下文，实现高质量的外推。大量实验表明，studentSplat在单视图新视图重建质量上达到了最先进的水平，并在场景层面与多视图方法的性能相当。此外，studentSplat在作为自监督单视图深度估计方法时也表现出竞争力，突显了其在通用单视图3D理解任务中的潜力。
英文摘要：
Recent advance in feed-forward 3D Gaussian splatting has enable remarkable multi-view 3D scene reconstruction or single-view 3D object reconstruction but single-view 3D scene reconstruction remain under-explored due to inherited ambiguity in single-view. We present \textbf{studentSplat}, a single-view 3D Gaussian splatting method for scene reconstruction. To overcome the scale ambiguity and extrapolation problems inherent in novel-view supervision from a single input, we introduce two techniques: 1) a teacher-student architecture where a multi-view teacher model provides geometric supervision to the single-view student during training, addressing scale ambiguity and encourage geometric validity; and 2) an extrapolation network that completes missing scene context, enabling high-quality extrapolation. Extensive experiments show studentSplat achieves state-of-the-art single-view novel-view reconstruction quality and comparable performance to multi-view methods at the scene level. Furthermore, studentSplat demonstrates competitive performance as a self-supervised single-view depth estimation method, highlighting its potential for general single-view 3D understanding tasks.
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。
