  Neural radiance fields (NeRFs) show potential for transforming images
captured worldwide into immersive 3D visual experiences. However, most of this
captured visual data remains siloed in our camera rolls as these images contain
personal details. Even if made public, the problem of learning 3D
representations of billions of scenes captured daily in a centralized manner is
computationally intractable. Our approach, DecentNeRF, is the first attempt at
decentralized, crowd-sourced NeRFs that require $\sim 10^4\times$ less server
computing for a scene than a centralized approach. Instead of sending the raw
data, our approach requires users to send a 3D representation, distributing the
high computation cost of training centralized NeRFs between the users. It
learns photorealistic scene representations by decomposing users' 3D views into
personal and global NeRFs and a novel optimally weighted aggregation of only
the latter. We validate the advantage of our approach to learn NeRFs with
photorealism and minimal server computation cost on structured synthetic and
real-world photo tourism datasets. We further analyze how secure aggregation of
global NeRFs in DecentNeRF minimizes the undesired reconstruction of personal
content by the server.
