Neural Radiance Field (NeRF) has recently emerged as a powerful representation to synthesize photorealistic novel views. While showing impressive performance, it relies on the availability of dense input views with highly accurate camera poses, thus limiting its application in real-world scenarios. In this work, we introduce Sparse Pose Adjusting Radiance Field (SPARF), to address the challenge of novel-view synthesis given only few wide-baseline input images (as low as 3) with noisy camera poses. Our approach exploits multi-view geometry constraints in order to jointly learn the NeRF and refine the camera poses. By relying on pixel matches extracted between the input views, our multi-view correspondence objective enforces the optimized scene and camera poses to converge to a global and geometrically accurate solution. Our depth consistency loss further encourages the reconstructed scene to be consistent from any viewpoint. Our approach sets a new state of the art in the sparse-view regime on multiple challenging datasets.

翻译：最近,神经辐射场(NeRF)成为了合成光现实新观点的有力代表。在展示了令人印象深刻的性能的同时,它依赖大量输入视图的可用性能,并具有高度精确的摄像头配置,从而限制了其在现实世界情景中的应用。在这项工作中,我们引入了“松片调整光度场 ” (SPARF), 以应对新观点合成的挑战,因为只有很少的宽基线输入图像(只有3个低,只有3个低)和噪音摄像头构成。我们的方法利用了多视几何几何测量限制,以便共同学习NeRF,并改进摄像头的配置。我们多视通信目标依靠在输入视图之间提取的像素匹配,强制优化场和摄像头形成接近全球和几何精确的解决方案。我们的深度一致性损失进一步鼓励重建的场景与任何观点保持一致。我们的方法在多挑战的数据集上设置了稀有的艺术的新状态。