用教师模型指导学生模型，实现单视角3D场景高质量重建
studentSplat: Your Student Model Learns Single-view 3D Gaussian Splatting
- 教师-学生架构提供几何监督，解决单视角尺度模糊问题
- 新提出外推网络补全缺失场景，支持高质量视角生成
- 适合需要单视角3D理解的科研与工业应用
单视角3D场景重建因视角信息不足而面临挑战。本文提出studentSplat，一种基于单视角3D高斯点阵的重建方法。为克服单输入带来的尺度模糊和视图外推难题，引入两项技术：1）采用多视角教师模型在训练中为单视角学生模型提供几何监督，有效缓解尺度不确定性并提升几何合理性；2）设计外推网络，自动补全缺失的场景上下文，实现高质量的新视角生成。大量实验表明，studentSplat在单视角新视图重建质量上达到当前最优，且在场景级重建上性能媲美多视角方法。此外，该方法在自监督单视角深度估计任务中表现优异，展现出在通用单视角3D理解任务中的潜力。
原文摘要 · Abstract (English)
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.
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