  Photo-realistic modeling and rendering of fuzzy objects with complex opacity
are critical for numerous immersive VR/AR applications, but it suffers from
strong view-dependent brightness, color. In this paper, we propose a novel
scheme to generate opacity radiance fields with a convolutional neural renderer
for fuzzy objects, which is the first to combine both explicit opacity
supervision and convolutional mechanism into the neural radiance field
framework so as to enable high-quality appearance and global consistent alpha
mattes generation in arbitrary novel views. More specifically, we propose an
efficient sampling strategy along with both the camera rays and image plane,
which enables efficient radiance field sampling and learning in a patch-wise
manner, as well as a novel volumetric feature integration scheme that generates
per-patch hybrid feature embeddings to reconstruct the view-consistent
fine-detailed appearance and opacity output. We further adopt a patch-wise
adversarial training scheme to preserve both high-frequency appearance and
opacity details in a self-supervised framework. We also introduce an effective
multi-view image capture system to capture high-quality color and alpha maps
for challenging fuzzy objects. Extensive experiments on existing and our new
challenging fuzzy object dataset demonstrate that our method achieves
photo-realistic, globally consistent, and fined detailed appearance and opacity
free-viewpoint rendering for various fuzzy objects.
