See Through the Occlusions: Few-Shot Gaussian Splatting with Layered Amodal Supervision

High-quality three-dimensional (3D) reconstruction from sparse views is critical for applications such as virtual and augmented reality, robotics, and digital content creation. While methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have shown strong performance in novel view synthesis, they struggle in few-shot settings, especially when scenes contain large occluded or unseen regions. The lack of explicit supervision for hidden content limits reconstruction completeness and realism. We propose See-Through-the-Occlusion Gaussian Splatting (STO-GS), a novel framework that rethinks occlusion modeling in static scenes. Drawing inspiration from four-dimensional Gaussian Splatting (4DGS), we reinterpret time as a proxy for occlusion depth and apply deformation-based opacity modulation to recover hidden layers. To provide supervision, we generate amodal views via diffusion-based inpainting, exposing occluded structures for training. A two-stage layered training pipeline further refines the reconstruction, with a multi-layer perceptron (MLP) adjusting Gaussian opacity across occlusion layers. STO-GS improves occlusion-aware reconstruction and achieves superior performance over existing few-shot 3DGS baselines, including a 0.51 dB gain on challenging datasets.