EdgeNeRF: Edge-Guided Regularization for Neural Radiance Fields from Sparse Views
This work addresses a domain-specific problem in 3D reconstruction for sparse-view scenarios, offering an incremental improvement over existing methods.
The paper tackles the problem of geometric artifacts in Neural Radiance Fields (NeRF) under sparse inputs by proposing EdgeNeRF, an edge-guided regularization method that preserves boundary details, achieving superior performance on LLFF and DTU datasets.
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.