Dimensional discoveries: unveiling the potential of 3D heritage point clouds with a robust ontology framework

3D point clouds feature valuable geometric and, often, radiometric and semantic information to support studies, analyses and understanding of the surveyed scene. Due to their generally large size, the use and interpretation of point clouds could be problematic. User-friendly and quick approaches for querying these valuable datasets and retrieving information could surely support end-users, in particular in the heritage sector. This work presents an ontology-based approach to facilitate the query and use of 3D heritage point clouds by means of sets of rules in order to infer properties and characteristics of the surveyed scene. Our approach is focused on linking together 3D spatial data and expert knowledge, in a way that the ontology can elaborate, represent, enrich and query a given point cloud. Results show how different queries can be set-up and how the procedure can be replicated to various queries and datasets

NERF FOR HERITAGE 3D RECONSTRUCTION

Conventional or learning-based 3D reconstruction methods from images have clearly shown their potential for 3D heritage documentation. Nevertheless, Neural Radiance Field (NeRF) approaches are recently revolutionising the way a scene can be rendered or reconstructed in 3D from a set of oriented images. Therefore the paper wants to review some of the last NeRF methods applied to various cultural heritage datasets collected with smartphone videos, touristic approaches or reflex cameras. Firstly several NeRF methods are evaluated. It turned out that Instant-NGP and Nerfacto methods achieved the best outcomes, outperforming all other methods significantly. Successively qualitative and quantitative analyses are performed on various datasets, revealing the good performances of NeRF methods, in particular for areas with uniform texture or shining surfaces, as well as for small datasets of lost artefacts. This is for sure opening new frontiers for 3D documentation, visualization and communication purposes of digital heritage

Combining image and point cloud segmentation to improve heritage understanding

Current 2D and 3D semantic segmentation frameworks are developed and trained on specific benchmark datasets, often rich of synthetic data, and when they are applied to complex and real-world heritage scenarios they offer much lower accuracy than expected. In this work, we present and demonstrate an early and late fusion of methods for semantic segmentation in cultural heritage applications. We rely on image datasets, point clouds and BIM models. The early fusion utilizes multi-view rendering to generate RGBD imagery of the scene. In contrast, the late fusion approach merges image-based segmentation with a Point Transformer applied to point clouds. Two scenarios are considered and inference results show that predictions are primarily influenced by whether the scene has a predominantly geometric or texture-based signature, underscoring the necessity of fusion methods

NERFBK: A HOLISTIC DATASET FOR BENCHMARKING NERF-BASED 3D RECONSTRUCTION

Neural Radiance Field methods are innovative solutions to derive 3D data from a set of oriented images. This paper introduces new real and synthetic image datasets - called NeRFBK - specifically designed for testing and comparing NeRF-based 3D reconstruction algorithms. More and more reconstruction algorithms and techniques are available nowadays, raising the need to evaluate and compare the quality of derived 3D products currently used in various domains and applications. However, gathering diverse data with precise ground truth is challenging and may not encompass all relevant applications. The NeRFBK dataset addresses this issue by providing multi-scale, indoor and outdoor datasets with high-resolution images and videos and camera parameters for testing and comparing NeRF-based algorithms. This paper presents the design and creation of the NeRFBK set of data, various examples and application scenarios, and highlights its potential for advancing the field of 3D reconstruction

Tamoxifen in treatment of hepatocellular carcinoma: a randomised controlled trial

Background Results from small randomised trials on tamoxifen in the treatment of hepatocellular carcinoma (HCC) are conflicting, We studied whether the addition of tamoxifen to best supportive care prolongs survival of patients with HCC. Methods Patients with any stage of HCC were eligible, irrespective of locoregional treatment. Randomisation was centralised, with a minimisation procedure accounting for centre, evidence of disease, and time from diagnosis. Patients were randomly allocated best supportive care alone or in addition to tamoxifen, Tamoxifen was given orally, 40 mg per day, from randomisation until death. Results 496 patients from 30 institutions were randomly allocated treatment from January, 1995, to January, 1997. Information was available for 477 patients. By Sept 15, 1997, 119 (50%) of 240 and 130 (55%) of 237 patients had died in the control and tamoxifen arms, respectively. Median survival was 16 months and 15 months (p=0.54), respectively, No differences were found within subgroups defined by prognostic variables. Relative hazard of death for patients receiving tamoxifen was 1.07 (95% CI 0.83-1.39). Interpretation Our findings show that tamoxifen is not effective in prolonging survival of patients with HCC

Mechanisms of Intragastric pH Sensing

Luminal amino acids and lack of luminal acidity as a result of acid neutralization by intragastric foodstuffs are powerful signals for acid secretion. Although the hormonal and neural pathways underlying this regulatory mechanism are well understood, the nature of the gastric luminal pH sensor has been enigmatic. In clinical studies, high pH, tryptic peptides, and luminal divalent metals (Ca2+ and Mg2+) increase gastrin release and acid production. The calcium-sensing receptor (CaSR), first described in the parathyroid gland but expressed on gastric G cells, is a logical candidate for the gastric acid sensor. Because CaSR ligands include amino acids and divalent metals, and because extracellular pH affects ligand binding in the pH range of the gastric content, its pH, metal, and nutrient-sensing functions are consistent with physiologic observations. The CaSR is thus an attractive candidate for the gastric luminal sensor that is part of the neuroendocrine negative regulatory loop for acid secretion