Few-Shot NeRF-Based View Synthesis for Viewpoint-Biased Camera Pose Estimation

Recently, several works have paid attention to view synthesis by neural radiance fields (NeRF) to improve camera pose estimation. Among them, LENS and Direct-PoseNet synthesize novel views from pre-trained NeRF and then train the pose regression convolutional network using real observations and the augmented synthetic views for better localization. Therefore, the performance depends on the three-dimensional (3D) consistency and the image quality of novel views. Especially, localization tends to fail if a diverse and high-quality training set is unavailable. To solve this issue, we tackle the problem of learning camera pose regressor from the viewpoint-biased and limited training set. We propose augmenting the regressor's training set using a few-shot NeRF instead of an original NeRF, which is employed in the previous frameworks. We can render high-quality novel views with a consistent 3D structure for stable training of the regressor. The experiments show that few-shot NeRF is an effective data augmenter for camera pose estimation under the viewpoint-biased limited training set.