针对稀疏视角重建，提出新方法提升3D高斯点云质量与细节表现。
Optimizing 3D Gaussian Splatting for Sparse Viewpoint Scene Reconstruction
- 引入3D高斯平滑滤波器抑制高频伪影
- 结合深度梯度先验与动态深度掩码增强边缘清晰度
- 融合2D扩散模型与分数蒸馏损失提升新视角几何一致性
3D高斯点阵（3DGS）作为新兴的三维场景表示方法，相比神经辐射场（NeRF）具有更低的计算开销。然而，3DGS在稀疏视角条件下易产生高频伪影，性能不佳，限制了其在机器人学与计算机视觉中的应用。为此，本文提出一种名为SVS-GS的稀疏视角场景重建新框架，通过集成3D高斯平滑滤波器以抑制伪影。同时，引入深度梯度轮廓先验（DGPP）损失并结合动态深度掩码以锐化边缘，并采用2D扩散模型与分数蒸馏采样（SDS）损失提升新视角合成的几何一致性。在MipNeRF-360和SeaThru-NeRF数据集上的实验表明，SVS-GS显著提升了稀疏视角下的三维重建质量，为机器人与计算机视觉中的场景理解提供了高效可靠的解决方案。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has emerged as a promising approach for 3D scene representation, offering a reduction in computational overhead compared to Neural Radiance Fields (NeRF). However, 3DGS is susceptible to high-frequency artifacts and demonstrates suboptimal performance under sparse viewpoint conditions, thereby limiting its applicability in robotics and computer vision. To address these limitations, we introduce SVS-GS, a novel framework for Sparse Viewpoint Scene reconstruction that integrates a 3D Gaussian smoothing filter to suppress artifacts. Furthermore, our approach incorporates a Depth Gradient Profile Prior (DGPP) loss with a dynamic depth mask to sharpen edges and 2D diffusion with Score Distillation Sampling (SDS) loss to enhance geometric consistency in novel view synthesis. Experimental evaluations on the MipNeRF-360 and SeaThru-NeRF datasets demonstrate that SVS-GS markedly improves 3D reconstruction from sparse viewpoints, offering a robust and efficient solution for scene understanding in robotics and computer vision applications.
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