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：神经辐射场（NeRF）最近作为一种能够合成逼真新视角的强大表示方法而兴起。尽管表现出令人印象深刻的性能，但它依赖于密集输入视图和高度精确的相机姿态的可用性，从而限制了其在现实场景中的应用。在这项工作中，我们引入了稀疏姿态调整辐射场（SPARF），以解决在仅有少量宽基线输入图像（低至3张）且相机姿态带噪声的情况下进行新视角合成的挑战。我们的方法利用多视图几何约束来联合学习NeRF并优化相机姿态。通过依赖输入视图之间提取的像素匹配，我们的多视图对应目标函数强制优化后的场景和相机姿态收敛到一个全局且几何精确的解。我们的深度一致性损失进一步鼓励重建的场景从任何视角保持一致。我们的方法在多个具有挑战性的数据集上的稀疏视图设置中达到了新的最先进水平。