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Structure from Motion (SfM) is a computer vision technique used to reconstruct three-dimensional (3D) structures from a series of two-dimensional (2D) images or video frames. However, SfM tools struggle with transparent objects, reflective surfaces, and low-resolution frames. In such situations, image-based interactive 3D modeling software packages are employed to model 3D objects and measure dimensions. Our contributions to this work are twofold. First, we have introduced new tools to improve 3D modeling software packages; such tools are aimed at easing the workload for users. Second, we have conducted a comprehensive user study to evaluate the efficacy of popular 3d modeling software packages. The task is to measure certain dimensions for which ground truth measurements are already known. A relative error is calculated for every measurement. The evaluation of each software tool is done through survey form, event logs, and measurement relative error. The results of this user study clearly show that our approach to 3D modeling using multiple images has a lower relative error and produces higher quality 3D models than other software packages. In addition, it shows our new tools reduce the required time for completing a task.

Efficiency and efficacy are desirable properties for any evaluation metric having to do with Standard Dynamic Range (SDR) imaging or with High Dynamic Range (HDR) imaging. However, it is a daunting task to satisfy both properties simultaneously. On the one side, existing evaluation metrics like HDR-VDP 2.2 can accurately mimic the Human Visual System (HVS), but this typically comes at a very high computational cost. On the other side, computationally cheaper alternatives (e.g., PSNR, MSE, etc.) fail to capture many crucial aspects of the HVS. In this work, we present NoR-VDPNet++, a deep learning architecture for converting full-reference accurate metrics into no-reference metrics thus reducing the computational burden. We show NoR-VDPNet++ can be successfully employed in different application scenarios.

We present MoReLab, a tool for user-assisted 3D reconstruction. This reconstruction requires an understanding of the shapes of the desired objects. Our experiments demonstrate that existing Structure from Motion (SfM) software packages fail to estimate accurate 3D models in low-quality videos due to several issues such as low resolution, featureless surfaces, low lighting, etc. In such scenarios, which are common for industrial utility companies, user assistance becomes necessary to create reliable 3D models. In our system, the user first needs to add features and correspondences manually on multiple video frames. Then, classic camera calibration and bundle adjustment are applied. At this point, MoReLab provides several primitive shape tools such as rectangles, cylinders, curved cylinders, etc., to model different parts of the scene and export 3D meshes. These shapes are essential for modeling industrial equipment whose videos are typically captured by utility companies with old video cameras (low resolution, compression artifacts, etc.) and in disadvantageous lighting conditions (low lighting, torchlight attached to the video camera, etc.). We evaluate our tool on real industrial case scenarios and compare it against existing approaches. Visual comparisons and quantitative results show that MoReLab achieves superior results with regard to other user-interactive 3D modeling tools.

Pottery is of fundamental importance for understanding archaeological contexts, facilitating the understanding of production, trade flows, and social interactions. Pottery characterisation and the classification of ceramics is still a manual process, reliant on analogue catalogues created by specialists, held in archives and libraries. The ArchAIDE project worked to streamline, optimise and economise the mundane aspects of these processes, using the latest automatic image recognition technology, while retaining key decision points necessary to create trusted results. Specifically, ArchAIDE worked to support classification and interpretation work (during both fieldwork and post-excavation analysis) with an innovative app for tablets and smartphones. This article summarises the work of this three-year project, funded by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement N.693548, with a consortium of partners representing both the academic and industry-led ICT (Information and Communications Technology) domains, and the academic and development-led archaeology domains. The collaborative work of the archaeological and technical partners created a pipeline where potsherds are photographed, their characteristics compared against a trained neural network, and the results returned with suggested matches from a comparative collection with typical pottery types and characteristics. Once the correct type is identified, all relevant information for that type is linked to the new sherd and stored within a database that can be shared online. ArchAIDE integrated a variety of novel and best-practice approaches, both in the creation of the app, and the communication of the project to a range of stakeholders.

This paper discusses a case study on the use of augmented reality (AR) within the context of cultural heritage. We implemented an iOS app for markerless AR that will be exhibited at the MUST museum in Lecce, Italy. The app shows a rich 3D reconstruction of the Roman amphitheater, which is nowadays only partially visible. The use of state-of-the-art algorithms in computer graphics and computer vision allows the viewing and the exploration of the ancient theater in real-time.