  The efficient representation, transmission, and reconstruction of
three-dimensional (3D) contents are becoming increasingly important for
sixth-generation (6G) networks that aim to merge virtual and physical worlds
for offering immersive communication experiences. Neural radiance field (NeRF)
and 3D Gaussian splatting (3D-GS) have recently emerged as two promising 3D
representation techniques based on radiance field rendering, which are able to
provide photorealistic rendering results for complex scenes. Therefore,
embracing NeRF and 3D-GS in 6G networks is envisioned to be a prominent
solution to support emerging 3D applications with enhanced quality of
experience. This paper provides a comprehensive overview on the integration of
NeRF and 3D-GS in 6G. First, we review the basics of the radiance field
rendering techniques, and highlight their applications and implementation
challenges over wireless networks. Next, we consider the over-the-air training
of NeRF and 3D-GS models over wireless networks by presenting various learning
techniques. We particularly focus on the federated learning design over a
hierarchical device-edge-cloud architecture, which is suitable for exploiting
distributed data and computing resources over 6G networks to train large models
representing large-scale scenes. Then, we consider the over-the-air rendering
of NeRF and 3D-GS models at wireless network edge. We present three practical
rendering architectures, namely local, remote, and co-rendering, respectively,
and provide model compression approaches to facilitate the transmission of
radiance field models for rendering. We also present rendering acceleration
approaches and joint computation and communication designs to enhance the
rendering efficiency. In a case study, we propose a new semantic communication
enabled 3D content transmission design.
