Neural Radiance Fields (NeRFs) have achieved impressive results in novel view synthesis and surface reconstruction tasks. However, their performance suffers under challenging scenarios with sparse input views. We present CorresNeRF, a novel method that leverages image correspondence priors computed by off-the-shelf methods to supervise NeRF training. We design adaptive processes for augmentation and filtering to generate dense and high-quality correspondences. The correspondences are then used to regularize NeRF training via the correspondence pixel reprojection and depth loss terms. We evaluate our methods on novel view synthesis and surface reconstruction tasks with density-based and SDF-based NeRF models on different datasets. Our method outperforms previous methods in both photometric and geometric metrics. We show that this simple yet effective technique of using correspondence priors can be applied as a plug-and-play module across different NeRF variants. The project page is at https://yxlao.github.io/corres-nerf.

翻译：神经辐射场（NeRF）在新视角合成和表面重建任务中取得了显著成果。然而，在稀疏输入视图等挑战性场景下，其性能会受到影响。我们提出CorresNeRF，这是一种利用现成方法计算的图像对应先验来监督NeRF训练的新方法。我们设计了自适应增强和过滤过程，以生成了密集且高质量的对应关系。随后，通过对应像素重投影和深度损失项将这些对应关系用于正则化NeRF训练。我们在不同数据集上，基于密度和符号距离函数（SDF）的NeRF模型，对新视角合成和表面重建任务进行了方法评估。我们的方法在光度指标和几何指标上均优于先前方法。我们表明，这种使用对应先验的简单而有效技术可以作为即插即用模块应用于不同的NeRF变体。项目页面位于https://yxlao.github.io/corres-nerf。