## 2 State of the Art  
This section aims to provide an overview of the field of 3D DL, the predecessor of GS and SuGaR, as well as a summary of the research advancements and papers that have been published over the last few years, a timeline whereof is shown in Figure 1. As its name suggests, 3D DL is a field within DL that works with three-dimensional data types and representations. Unlike other fields of computer vision that work with 2D images generally represented as pixel tensors, 3D models have different types of representations, such as point clouds, voxels, polygonal meshes, or neural representations, which open multiple opportunities when working with them. RFR is a Novel View Synthesis, an ML application to the field of computer graphics that takes scene data captured from photos or videos and builds 3D rendered representation models. Neural field rendering (NeRF) [26] had been the historical SOTA RFR incarnation [23]. Due to the scarcity of 3D datasets, most papers have studied the reconstruction of 3D objects from images taken from different angles, measuring the reconstruction error on projections of the object. This means 3D-\"constructing\" the predicted geometry, rasterizing it, and comparing the output images with the original ones from the same camera angles. Two of the main challenges of 3D DL are, firstly, to find the balance between the amount of input data (images) needed and the detail level of the output 3D model, and, secondly, to reduce model training computational cost, as it is usually very high. Both points must be considered when developing and testing these kinds of AI models. Historically, 3D DL started with 3D reconstruction from both single-view and multi-view images, such as perspective transformer nets, which is a volumetric scene reconstruction solution based on voxels from single-view images using a projection function to compare 2D images, obtaining SOTA results [27]. Other methods emerged using voxels and polygonal meshes, utilizing the OpenGL renderer [28]. Later, an algorithm for applying gradient descent to polygonal mesh rendering and mesh reconstruction from single-view images was devised, providing SOTA results [29]. NeRF established itself as the SOTA standard in Novel View Synthesis methods by using a multi-layer perceptron (MLP) network as a form of compact and continuous representation of a single 3D scene. From multi-view images, NeRF interpolates the rest of the views of the scene [26]. Around 2020, PyTorch3D [30] began being widely adopted and would later become the standard library for 3D DL. It was created in PyTorch with operators and functions specialized in 3D DL. Furthermore, a model called PolyGen performed 3D reconstruction of polygonal meshes from.obj files using the Transformer architecture [31,32]. In addition, a stream of NeRF-stemming methods emerged, cementing its dominance, such as NeRF in the Wild, which is an evolution of the NeRF model [33]. The first was Instant Neural Graphics Primitives (INGP), a NeRF-like model improved via modifications in gradient computation and the use of hash-grids [34] that attempted to cut NeRF training times, yet did not provide SOTA quality. Then, Neuralangelo [35] was proposed, itself based on INGP, which optimizes training speeds while not achieving SOTA image quality either. Later, DreamFusion was devised, a model that implemented text-to-3D generative capabilities from pre-trained diffusion and NeRF models [36]. Finally, NeRFMeshing [37] emerged, a signed surface approx. network (SSAN) model that, based on the implicit representation of another NeRF model, rendered a 3D polygonal mesh. Until recently, NeRF methods provided the highest visual quality and were considered the SOTA in the field of RFR, most notably Mip-NeRF360 [38], which can take as long as 48 h of training time, its main limitation. This landscape changed when a new model, GS, devised by [23], achieved similar Mip-NeRF360-SOTA quality with training and processing times reduced by a factor of 10.