  Novel view synthesis is a long-standing problem that revolves around
rendering frames of scenes from novel camera viewpoints. Volumetric approaches
provide a solution for modeling occlusions through the explicit 3D
representation of the camera frustum. Multi-plane Images (MPI) are volumetric
methods that represent the scene using front-parallel planes at distinct depths
but suffer from depth discretization leading to a 2.D scene representation.
Another line of approach relies on implicit 3D scene representations. Neural
Radiance Fields (NeRF) utilize neural networks for encapsulating the continuous
3D scene structure within the network weights achieving photorealistic
synthesis results, however, methods are constrained to per-scene optimization
settings which are inefficient in practice. Multi-plane Neural Radiance Fields
(MINE) open the door for combining implicit and explicit scene representations.
It enables continuous 3D scene representations, especially in the depth
dimension, while utilizing the input image features to avoid per-scene
optimization. The main drawback of the current literature work in this domain
is being constrained to single-view input, limiting the synthesis ability to
narrow viewpoint ranges. In this work, we thoroughly examine the performance,
generalization, and efficiency of single-view multi-plane neural radiance
fields. In addition, we propose a new multiplane NeRF architecture that accepts
multiple views to improve the synthesis results and expand the viewing range.
Features from the input source frames are effectively fused through a proposed
attention-aware fusion module to highlight important information from different
viewpoints. Experiments show the effectiveness of attention-based fusion and
the promising outcomes of our proposed method when compared to multi-view NeRF
and MPI techniques.
