  This paper presents a flexible representation of neural radiance fields based
on multi-plane images (MPI), for high-quality view synthesis of complex scenes.
MPI with Normalized Device Coordinate (NDC) parameterization is widely used in
NeRF learning for its simple definition, easy calculation, and powerful ability
to represent unbounded scenes. However, existing NeRF works that adopt MPI
representation for novel view synthesis can only handle simple forward-facing
unbounded scenes, where the input cameras are all observing in similar
directions with small relative translations. Hence, extending these MPI-based
methods to more complex scenes like large-range or even 360-degree scenes is
very challenging. In this paper, we explore the potential of MPI and show that
MPI can synthesize high-quality novel views of complex scenes with diverse
camera distributions and view directions, which are not only limited to simple
forward-facing scenes. Our key idea is to encode the neural radiance field with
multiple MPIs facing different directions and blend them with an adaptive
blending operation. For each region of the scene, the blending operation gives
larger blending weights to those advantaged MPIs with stronger local
representation abilities while giving lower weights to those with weaker
representation abilities. Such blending operation automatically modulates the
multiple MPIs to appropriately represent the diverse local density and color
information. Experiments on the KITTI dataset and ScanNet dataset demonstrate
that our proposed MMPI synthesizes high-quality images from diverse camera pose
distributions and is fast to train, outperforming the previous fast-training
NeRF methods for novel view synthesis. Moreover, we show that MMPI can encode
extremely long trajectories and produce novel view renderings, demonstrating
its potential in applications like autonomous driving.
