3D Gaussian Splatting (3DGS) has recently emerged as an efficient, high-fidelity representation for real-time scene reconstruction and rendering. However, extending 3DGS to sparse-view settings remains challenging because of supervision scarcity and overfitting caused by limited viewpoint coverage. In this paper, we present CuriGS, a curriculum-guided framework for sparse-view 3D reconstruction using 3DGS. CuriGS addresses the core challenge of sparse-view synthesis by introducing student views: pseudo-views sampled around ground-truth poses (teacher). For each teacher, we generate multiple groups of student views with different perturbation levels. During training, we follow a curriculum schedule that gradually unlocks higher perturbation level, randomly sampling candidate students from the active level to assist training. Each sampled student is regularized via depth-correlation and co-regularization, and evaluated using a multi-signal metric that combines SSIM, LPIPS, and an image-quality measure. For every teacher and perturbation level, we periodically retain the best-performing students and promote those that satisfy a predefined quality threshold to the training set, resulting in a stable augmentation of sparse training views. Experimental results show that CuriGS outperforms state-of-the-art baselines in both rendering fidelity and geometric consistency across various synthetic and real sparse-view scenes.
Overall Framework. The pipeline consists of three key stages: (1) student view generation, where pseudo-camera poses are sampled around teacher views with multiple perturbation magnitudes, as detailed in (B); (2) curriculum scheduling, which gradually unlocks perturbation levels during training to progressively expand viewpoint diversity; and (3) student view evaluation and promotion, where each candidate is scored using perceptual (LPIPS), structural (SSIM), and no-reference quality metrics. Only the best student at each perturbation level that passes the evaluation criteria is promoted to the training set, as illustrated in (C). This curriculum-guided process enhances geometric consistency and rendering fidelity under sparse supervision.
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