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

EVALUATING MONOCULAR DEPTH ESTIMATION METHODS

Depth estimation from monocular images has become a prominent focus in photogrammetry and computer vision research. Monocular Depth Estimation (MDE), which involves determining depth from a single RGB image, offers numerous advantages, including applications in simultaneous localization and mapping (SLAM), scene comprehension, 3D modeling, robotics, and autonomous driving. Depth information retrieval becomes especially crucial in situations where other sources like stereo images, optical flow, or point clouds are not available. In contrast to traditional stereo or multi-view methods, MDE techniques require fewer computational resources and smaller datasets. This research work presents a comprehensive analysis and evaluation of some state-of-the-art MDE methods, considering their ability to infer depth information in terrestrial images. The evaluation includes quantitative assessments using ground truth data, including 3D analyses and inference time

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

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

The Legacy of Sycamore Gap: The Potential of Photogrammetric AI for Reverse Engineering Lost Heritage with Crowdsourced Data

\ua9 Author(s) 2024.The orientation of crowdsourced and multi-temporal image datasets presents a challenging task for traditional photogrammetry. Indeed, traditional image matching approaches often struggle to find accurate and reliable tie points in images that appear significantly different from one another. In this paper, in order to preserve the memory of the Sycamore Gap tree, a symbol of Hadrian\u27s Wall that was felled in an act of vandalism in September 2023, deep-learning-based features trained specifically on challenging image datasets were employed to overcome limitations of traditional matching approaches. We demonstrate how unordered crowdsourced images and UAV videos can be oriented and used for 3D reconstruction purposes, together with a recently acquired terrestrial laser scanner point cloud for scaling and referencing. This allows the memory of the Sycamore Gap tree to live on and exhibits the potential of photogrammetric AI (Artificial Intelligence) for reverse engineering lost heritage

Herpes Simplex Virus 1 and 2 Infections during Differentiation of Human Cortical Neurons

Herpes simplex virus 1 (HSV-1) and 2 (HSV-2) can infect the central nervous system (CNS) with dire consequences; in children and adults, HSV-1 may cause focal encephalitis, while HSV-2 causes meningitis. In neonates, both viruses can cause severe, disseminated CNS infections with high mortality rates. Here, we differentiated human induced pluripotent stem cells (iPSCs) towards cortical neurons for infection with clinical CNS strains of HSV-1 or HSV-2. Progenies from both viruses were produced at equal quantities in iPSCs, neuroprogenitors and cortical neurons. HSV-1 and HSV-2 decreased viability of neuroprogenitors by 36.0% and 57.6% (p < 0.0001), respectively, 48 h post-infection, while cortical neurons were resilient to infection by both viruses. However, in these functional neurons, both HSV-1 and HSV-2 decreased gene expression of two markers of synaptic activity, CAMK2B and ARC, and affected synaptic activity negatively in multielectrode array experiments. However, unaltered secretion levels of the neurodegeneration markers tau and NfL suggested intact axonal integrity. Viral replication of both viruses was found after six days, coinciding with 6-fold and 22-fold increase in gene expression of cellular RNA polymerase II by HSV-1 and HSV-2, respectively. Our results suggest a resilience of human cortical neurons relative to the replication of HSV-1 and HSV-2

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