  Neural rendering has emerged as a powerful paradigm for synthesizing images,
offering many benefits over classical rendering by using neural networks to
reconstruct surfaces, represent shapes, and synthesize novel views, either for
objects or scenes. In this neural rendering, the environment is encoded into a
neural network. We believe that these new representations can be used to codify
the scene for a mobile robot. Therefore, in this work, we perform a comparison
between a trending neural rendering, called tiny-NeRF, and other volume
representations that are commonly used as maps in robotics, such as voxel maps,
point clouds, and triangular meshes. The target is to know the advantages and
disadvantages of neural representations in the robotics context. The comparison
is made in terms of spatial complexity and processing time to obtain a model.
Experiments show that tiny-NeRF requires three times less memory space compared
to other representations. In terms of processing time, tiny-NeRF takes about
six times more to compute the model.
