用深度特征一致性提升稀疏视角下的表面重建精度与速度
FatesGS: Fast and Accurate Sparse-View Surface Reconstruction using Gaussian Splatting with Depth-Feature Consistency
- 引入单视图深度排序与多视角投影特征约束
- 在DTU和BlendedMVS上实现60~200倍加速，精度领先
- 无需预训练，适合实时高精度三维重建场景
最近，高斯点阵在计算机视觉领域引发新趋势。除了新视角合成，还被拓展至多视角重建。最新方法虽能快速完成完整精细的表面重建，但仍需密集输入视角，稀疏视角下性能显著下降。我们发现，高斯原语易过拟合少数训练视图，导致噪声伪影和表面不完整。本文提出一种创新的稀疏视角重建框架，利用视内深度分布一致性和多视角特征一致性，实现高精度表面重建。具体地，通过单视图深度排序信息监督块内深度分布一致性，并采用平滑损失增强分布连续性；为实现更精细重建，通过多视角投影特征优化深度绝对位置。在DTU和BlendedMVS上的大量实验表明，本方法相比当前最优方法提速60至200倍，实现快速且细粒度的网格重建，无需昂贵预训练。
原文摘要 · Abstract (English)
Recently, Gaussian Splatting has sparked a new trend in the field of computer vision. Apart from novel view synthesis, it has also been extended to the area of multi-view reconstruction. The latest methods facilitate complete, detailed surface reconstruction while ensuring fast training speed. However, these methods still require dense input views, and their output quality significantly degrades with sparse views. We observed that the Gaussian primitives tend to overfit the few training views, leading to noisy floaters and incomplete reconstruction surfaces. In this paper, we present an innovative sparse-view reconstruction framework that leverages intra-view depth and multi-view feature consistency to achieve remarkably accurate surface reconstruction. Specifically, we utilize monocular depth ranking information to supervise the consistency of depth distribution within patches and employ a smoothness loss to enhance the continuity of the distribution. To achieve finer surface reconstruction, we optimize the absolute position of depth through multi-view projection features. Extensive experiments on DTU and BlendedMVS demonstrate that our method outperforms state-of-the-art methods with a speedup of 60x to 200x, achieving swift and fine-grained mesh reconstruction without the need for costly pre-training.
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