  We introduce ViewNeRF, a Neural Radiance Field-based viewpoint estimation
method that learns to predict category-level viewpoints directly from images
during training. While NeRF is usually trained with ground-truth camera poses,
multiple extensions have been proposed to reduce the need for this expensive
supervision. Nonetheless, most of these methods still struggle in complex
settings with large camera movements, and are restricted to single scenes, i.e.
they cannot be trained on a collection of scenes depicting the same object
category. To address these issues, our method uses an analysis by synthesis
approach, combining a conditional NeRF with a viewpoint predictor and a scene
encoder in order to produce self-supervised reconstructions for whole object
categories. Rather than focusing on high fidelity reconstruction, we target
efficient and accurate viewpoint prediction in complex scenarios, e.g.
360{\deg} rotation on real data. Our model shows competitive results on
synthetic and real datasets, both for single scenes and multi-instance
collections.
