This paper describes two procedures used to disseminate tangible cultural heritage through real-time 3D simulations providing accurate-scientific representations. The main idea is to create simple geometries (with low-poly count) and apply two different texture maps to them: a normal map and a displacement map. There are two ways to achieve models that fit with normal or displacement maps: with the former (normal maps), the number of polygons in the reality-based model may be dramatically reduced by decimation algorithms and then normals may be calculated by rendering them to texture solutions (baking). With the latter, a LOD model is needed; its topology has to be quad-dominant for it to be converted to a good quality subdivision surface (with consistent tangency and curvature all over). The subdivision surface is constructed using methodologies for the construction of assets borrowed from character animation: these techniques have been recently implemented in many entertainment applications known as “retopology”. The normal map is used as usual, in order to shade the surface of the model in a realistic way. The displacement map is used to finish, in real-time, the flat faces of the object, by adding the geometric detail missing in the low-poly models. The accuracy of the resulting geometry is progressively refined based on the distance from the viewing point, so the result is like a continuous level of detail, the only difference being that there is no need to create different 3D models for one and the same object. All geometric detail is calculated in real-time according to the displacement map. This approach can be used in Unity, a real-time 3D engine originally designed for developing computer games. It provides a powerful rendering engine, fully integrated with a complete set of intuitive tools and rapid workflows that allow users to easily create interactive 3D contents. With the release of Unity 4.0, new rendering features have been added, including DirectX 11 support. Real-time tessellation is a technique that can be applied by using such technology. Since the displacement and the resulting geometry are calculated by the GPU, the time-based execution cost of this technique is very low.

Public YOLO model releases typically provide high-level architectural descriptions and headline benchmark results but offer limited empirical attribution of performance to individual blocks under controlled training conditions. This paper presents a modular, block-level analysis of YOLO26’s object detection architecture, detailing the design, function, and contribution of each component. We systematically examine YOLO26’s convolutional modules, bottleneck-based refinement blocks, spatial pyramid pooling, and position-sensitive attention mechanisms. Each block is analyzed in terms of objective and internal flow. In parallel, we conduct targeted ablation studies to quantify the effect of key design choices on accuracy (mAP@0.50:0.95) and inference latency under a fixed seed-0, COCO-only, fully specified training and benchmarking protocol. Experiments use the MS COCOdataset with the standard train2017 split (≈118 k images) for training and the full val2017 split (5 k images) for evaluation. The result is a self-contained empirical architectural-attribution reference that supports interpretability, reproducibility, and evidence-based architectural decision-making for real-time detection models. Beyond isolated ablations, we further synthesize the best-performing design choices into combined YOLO26n configurations and compare them against the default baseline. The best combined configuration improves mAP@0.50:0.95 from 0.3933 to 0.3969, while introducing only a marginal latency increase from 0.99 ms to 1.00 ms under TensorRT FP16 benchmarking. This analysis identifies an improved accuracy–latency trade-off and provides an incremental architectural configuration contribution supported by controlled experiments. The study is, therefore, framed as a controlled empirical analysis and configuration-refinement study of YOLO26, rather than as the proposal of a new detector family or a claim of universal detector superiority.

Public YOLO model releases typically provide high-level architectural descriptions and headline benchmark results, but offer limited empirical attribution of performance to individual blocks under controlled training conditions. This paper presents a modular, block-level analysis of YOLO26’s object detection architecture, detailing the design, function, and contribution of each component. We systematically examine YOLO26’s convolutional modules, bottleneck-based refinement blocks, spatial pyramid pooling, and position-sensitive attention mechanisms. Each block is analyzed in terms of objective and internal flow. In parallel, we conduct targeted ablation studies to quantify the effect of key design choices on accuracy (mAP50–95) and inference latency under a fixed, fully specified training and benchmarking protocol. Experiments use the MS COCO [1] dataset with the standard train2017 split (≈118k images) for training and the full val2017 split (5k images) for evaluation. The result is a self-contained technical reference that supports interpretability, reproducibility, and evidence-based architectural decision-making for real-time detection models.

Advances in three-dimensional (3D) acquisition systems have introduced this technology to more fields of study, such as archaeology or architecture. In the architectural field, scanning a building is one of the first possible steps from which a 3D model can be obtained and can be later used for visualisation and/or feature analysis, thanks to computer-based pattern recognition tools. The automation of these tools allows for temporal savings and has become a strong aid for professionals, so that more and more methods are developed with this objective. In this article, a method for 3D mesh segmentation focused on the representation of historic buildings is proposed. This type of buildings is characterised by having singularities and features in façades, such as doors or windows. The main objective is to recognise these features, understanding them as those parts of the model that differ from the main structure of the building. The idea is to use a recognition algorithm for planar faces that allows users to create a graph showing the connectivity between them, therefore allowing the reflection of the shape of the 3Dmodel. At a later step, this graph is matched against some pre-defined graphs that represent the patterns to look for. Each coincidence between both graphs indicate the position of one of the characteristics sought. The developed method has proved to be effective for feature detection and suitable for inclusion in architectural surveying applications.