Abstract
3D Gaussian Splatting (3DGS) represents a significant advancement in the field of efficient and high-fidelity novel view synthesis. Despite recent progress, achieving accurate geometric reconstruction under sparse-view conditions remains a fundamental challenge. Existing methods often rely on non-local depth regularization, which fails to capture fine-grained structures and is highly sensitive to depth estimation noise. Furthermore, traditional smoothing methods neglect semantic boundaries and indiscriminately degrade essential edges and textures, consequently limiting the overall quality of reconstruction. In this work, we propose DET-GS, a unified depth and edge-aware regularization framework for 3D Gaussian Splatting. DET-GS introduces a hierarchical geometric depth supervision framework that adaptively enforces multi-level geometric consistency, significantly enhancing structural fidelity and robustness against depth estimation noise. To preserve scene boundaries, we design an edge-aware depth regularization guided by semantic masks derived from Canny edge detection. Furthermore, we introduce an RGB-guided edge-preserving Total Variation loss that selectively smooths homogeneous regions while rigorously retaining high-frequency details and textures. Extensive experiments demonstrate that DET-GS achieves substantial improvements in both geometric accuracy and visual fidelity, outperforming state-of-the-art (SOTA) methods on sparse-view novel view synthesis benchmarks.
Abstract (translated)
3D高斯点阵（3DGS）在高效且高质量的新视图合成领域中代表了重要进展。尽管最近取得了进步，但在稀疏视角条件下实现精确的几何重建仍然是一个基本挑战。现有方法通常依赖于非局部深度正则化技术，这无法捕捉细微结构，并且极易受到深度估计噪声的影响。此外，传统的平滑方法忽略了语义边界，并无差别地降低了重要的边缘和纹理质量，从而限制了整体重建效果的质量。 在本工作中，我们提出了DET-GS（深度与边缘感知的正则化框架），用于3D高斯点阵中的统一深度和边缘感知处理。DET-GS引入了一个层次化的几何深度监督框架，该框架能够自适应地强制执行多级几何一致性，从而极大地提升了结构保真度，并增强了对深度估计噪声的鲁棒性。为了保留场景边界，我们设计了一种基于语义掩膜（从Canny边缘检测导出）引导的边缘感知深度正则化方法。此外，我们引入了一种RGB指导下的边缘保持总变差损失，这种损失能够在平滑同质区域的同时严格保存高频细节和纹理。 广泛的实验表明，DET-GS在几何精度和视觉保真度方面均实现了显著改进，并且在稀疏视角的新视图合成基准测试中超越了最先进的方法。
URL
https://arxiv.org/abs/2508.04099
