Abstract
Novel View Synthesis (NVS) from sparse views remains a core challenge in 3D reconstruction, typically suffering from overfitting, geometric distortion, and incomplete scene recovery due to limited multi-view constraints. Although 3D Gaussian Splatting (3DGS) enables real-time, high-fidelity rendering, it suffers from floating artifacts and structural inconsistencies under sparse-input settings. To address these issues, we propose DWGS, a novel unified framework that enhances 3DGS for sparse-view synthesis by integrating robust structural cues, virtual view constraints, and occluded region completion. Our approach introduces three principal contributions: a Hybrid-Loss Depth Estimation module that leverages dense matching priors with reprojection, point propagation, and smoothness constraints to enforce multi-view consistency; a Bidirectional Warping Virtual View Synthesis method generates virtual training views to impose stronger geometric and photometric constraints; and an Occlusion-Aware Reconstruction component that utilizes depth-difference mask and a learning-based inpainting model to recover obscured regions. Extensive experiments on standard benchmarks (LLFF, Blender, and DTU) show that DWGS achieves a new state-of-the-art, achieving up to 21.13 dB PSNR and 0.189 LPIPS, while retaining real-time inference capabilities.
Abstract (translated)
从稀疏视角进行小说视图合成（NVS）在三维重建中仍然是一个核心挑战，通常会因多视角约束不足而出现过拟合、几何变形和场景恢复不完整等问题。尽管3D高斯点阵法（3DGS）能够实现实时且高质量的渲染，但在稀疏输入条件下却会出现漂浮伪影和结构一致性问题。为解决这些问题，我们提出了一种新的统一框架——DWGS，通过整合鲁棒的结构线索、虚拟视图约束以及被遮挡区域完成来增强3DGS在稀疏视角合成中的表现。 我们的方法主要贡献包括： 1. **混合损失深度估计模块**：该模块利用密集匹配先验和重投影、点传播和平滑度约束，以加强多视图一致性。 2. **双向卷积虚拟视图合成立法**：此方法生成虚拟训练视角，从而施加更强的几何和光度学限制。 3. **遮挡感知重建组件**：该模块利用深度差异掩码及学习型修复模型来恢复被遮挡区域。 在标准基准（LLFF、Blender 和 DTU）上的广泛实验表明，DWGS达到了新的最先进水平，在PSNR上最高达到21.13 dB，LPIPS值为0.189，同时保持了实时推断能力。
URL
https://arxiv.org/abs/2509.24893
