用边缘引导的正则化，让稀疏视角下的3D重建更清晰
EdgeNeRF: Edge-Guided Regularization for Neural Radiance Fields from Sparse Views
- 基于输入图像提取边缘，仅在非边缘区域施加深度与法向正则化
- 在LLFF和DTU数据集上显著提升边界清晰度，减少几何伪影
- 模块可即插即用，不增加训练时间，适合改进现有方法
神经辐射场（NeRF）在密集多视角场景中表现优异，但在稀疏输入下重建质量显著下降，产生几何伪影。现有方法采用全局深度正则化，导致几何边界细节丢失。为此，我们提出EdgeNeRF，一种基于边缘引导的稀疏视角3D重建算法。该方法利用深度与法向突变产生边缘的先验知识：首先从输入图像中提取边缘，然后仅在非边缘区域施加深度和法向正则化，增强几何一致性的同时保留边界高频细节。在LLFF和DTU数据集上的实验表明，EdgeNeRF在保持锐利几何边界和抑制伪影方面表现卓越。此外，所提出的边缘引导深度正则化模块可无缝集成到其他方法中，以极小的训练时间开销显著提升性能。代码已公开于https://github.com/skyhigh404/edgenerf。
原文摘要 · Abstract (English)
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.
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