论文标题
通过重新投影射线先验：改善新视图外推的神经辐射场
Ray Priors through Reprojection: Improving Neural Radiance Fields for Novel View Extrapolation
论文作者
论文摘要
神经辐射场（NERF）已成为代表场景和合成照片现实图像的有效范式。常规nerfs的主要局限性是，它们通常无法在与训练观点明显不同的新观点下产生高质量的效果。在本文中，我们研究了（1）训练图像可以很好地描述对象的新型观点外推设置，而不是利用很少的图像合成，并且（2）训练和测试观点的分布之间存在明显的差异。我们将Rapnerf（Ray Priors）作为解决方案。我们的见解是，3D表面任意可见的预测的固有外观应该是一致的。因此，我们提出了一项随机的射线铸造政策，该政策允许使用可见的视图训练看不见的观点。此外，我们表明，从观察到的射线观看方向预先计算的射线图集可以进一步提高外推视图的渲染质量。一个主要限制是Rapnerf将消除强大的观点效果，因为它利用了多视图一致性属性。
Neural Radiance Fields (NeRF) have emerged as a potent paradigm for representing scenes and synthesizing photo-realistic images. A main limitation of conventional NeRFs is that they often fail to produce high-quality renderings under novel viewpoints that are significantly different from the training viewpoints. In this paper, instead of exploiting few-shot image synthesis, we study the novel view extrapolation setting that (1) the training images can well describe an object, and (2) there is a notable discrepancy between the training and test viewpoints' distributions. We present RapNeRF (RAy Priors) as a solution. Our insight is that the inherent appearances of a 3D surface's arbitrary visible projections should be consistent. We thus propose a random ray casting policy that allows training unseen views using seen views. Furthermore, we show that a ray atlas pre-computed from the observed rays' viewing directions could further enhance the rendering quality for extrapolated views. A main limitation is that RapNeRF would remove the strong view-dependent effects because it leverages the multi-view consistency property.
