Neural Radiance Fields (NeRF) achieve remarkable performance in dense multi-view scenarios, but their reconstruction quality degrades significantly under sparse inputs due to geometric artifacts. Existing methods utilize global depth regularization to mitigate artifacts, leading to the loss of geometric boundary details. To address this problem, we propose EdgeNeRF, an edge-guided sparse-view 3D reconstruction algorithm. Our method leverages the prior that abrupt changes in depth and normals generate edges. Specifically, we first extract edges from input images, then apply depth and normal regularization constraints to non-edge regions, enhancing geometric consistency while preserving high-frequency details at boundaries. Experiments on LLFF and DTU datasets demonstrate EdgeNeRF's superior performance, particularly in retaining sharp geometric boundaries and suppressing artifacts. Additionally, the proposed edge-guided depth regularization module can be seamlessly integrated into other methods in a plug-and-play manner, significantly improving their performance without substantially increasing training time. Code is available at https://github.com/skyhigh404/edgenerf.

翻译：神经辐射场（NeRF）在密集多视角场景下表现出色，但在稀疏输入条件下，其重建质量会因几何伪影显著下降。现有方法通常采用全局深度正则化来抑制伪影，但这会导致几何边界细节的丢失。为解决这一问题，我们提出EdgeNeRF，一种基于边缘引导的稀疏视角三维重建算法。该方法利用深度与法向量的突变会生成边缘的先验知识：首先从输入图像中提取边缘，随后在非边缘区域施加深度与法向量正则化约束，从而在增强几何一致性的同时保留边界处的高频细节。在LLFF与DTU数据集上的实验表明，EdgeNeRF在保持锐利几何边界与抑制伪影方面具有优越性能。此外，所提出的边缘引导深度正则化模块能够以即插即用方式无缝集成到其他方法中，在不显著增加训练时间的前提下显著提升其性能。代码公开于 https://github.com/skyhigh404/edgenerf。