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.

翻译：神经渲染已成为图像合成领域的一种强大范式，通过利用神经网络重建表面、表示形状以及为物体或场景合成新视角，提供了优于经典渲染的诸多优势。在神经渲染中，环境被编码至神经网络内部。我们相信这些新型表示方法可用于为移动机器人构建场景编码。因此，本研究对当前流行的神经渲染方法——tiny-NeRF——与机器人制图中常用的体素地图、点云和三角网格等体积表示方法进行了比较。目标是明确神经表示在机器人领域中的优劣特性。比较指标涵盖空间复杂度与模型获取处理时间。实验表明，tiny-NeRF所需内存空间较其他表示方法减少三倍，但在处理时间上，tiny-NeRF计算模型所需时间约为其他方法的六倍。