  Fully unsupervised 3D representation learning has gained attention owing to
its advantages in data collection. A successful approach involves a
viewpoint-aware approach that learns an image distribution based on generative
models (e.g., generative adversarial networks (GANs)) while generating various
view images based on 3D-aware models (e.g., neural radiance fields (NeRFs)).
However, they require images with various views for training, and consequently,
their application to datasets with few or limited viewpoints remains a
challenge. As a complementary approach, an aperture rendering GAN (AR-GAN) that
employs a defocus cue was proposed. However, an AR-GAN is a CNN-based model and
represents a defocus independently from a viewpoint change despite its high
correlation, which is one of the reasons for its performance. As an alternative
to an AR-GAN, we propose an aperture rendering NeRF (AR-NeRF), which can
utilize viewpoint and defocus cues in a unified manner by representing both
factors in a common ray-tracing framework. Moreover, to learn defocus-aware and
defocus-independent representations in a disentangled manner, we propose
aperture randomized training, for which we learn to generate images while
randomizing the aperture size and latent codes independently. During our
experiments, we applied AR-NeRF to various natural image datasets, including
flower, bird, and face images, the results of which demonstrate the utility of
AR-NeRF for unsupervised learning of the depth and defocus effects.
