From ”Sapienza” to “Sapienza, State Archives in Rome”. A looping effect bringing back to the original source communication and culture by innovative and low cost 3D surveying, imaging systems and GIS applications

Applicazione di tecnologie mensorie integrate Low Cost,web GIS,applicazione di tecniche di Computational photography per la comunicazione e condivisione dei dati, sistemi di Cloud computing.Archiviazione Grandi DatiHigh Quality survey models, realized by multiple Low Cost methods and technologies, as a container to sharing Cultural and Archival Heritage, this is the aim guiding our research, here described in its primary applications. The SAPIENZA building, a XVI century masterpiece that represented the first unified headquarters of University in Rome, plays since year 1936, when the University moved to its newly edified campus, the role of the main venue for the State Archives. By the collaboration of a group of students of the Architecture Faculty, some integrated survey methods were applied on the monument with success. The beginning was the topographic survey, creating a reference on ground and along the monument for the upcoming applications, a GNNS RTK survey followed georeferencing points on the internal courtyard. Dense stereo matching photogrammetry is nowadays an accepted method for generating 3D survey models, accurate and scalable; it often substitutes 3D laser scanning for its low cost, so that it became our choice. Some 360°shots were planned for creating panoramic views of the double portico from the courtyard, plus additional single shots of some lateral spans and of pillars facing the court, as a single operation with a double finality: to create linked panotours with hotspots to web-linked databases, and 3D textured and georeferenced surface models, allowing to study the harmonic proportions of the classical architectural order. The use of free web Gis platforms, to load the work in Google Earth and the realization of low cost 3D prototypes of some representative parts, has been even performed

Fisheye Photogrammetry to Survey Narrow Spaces in Architecture and a Hypogea Environment

Nowadays, the increasing computation power of commercial grade processors has actively led to a vast spreading of image-based reconstruction software as well as its application in different disciplines. As a result, new frontiers regarding the use of photogrammetry in a vast range of investigation activities are being explored. This paper investigates the implementation of fisheye lenses in non-classical survey activities along with the related problematics. Fisheye lenses are outstanding because of their large field of view. This characteristic alone can be a game changer in reducing the amount of data required, thus speeding up the photogrammetric process when needed. Although they come at a cost, field of view (FOV), speed and manoeuvrability are key to the success of those optics as shown by two of the presented case studies: the survey of a very narrow spiral staircase located in the Duomo di Milano and the survey of a very narrow hypogea structure in Rome. A third case study, which deals with low-cost sensors, shows the metric evaluation of a commercial spherical camera equipped with fisheye lenses

SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving

In this paper, we introduce a deep encoder-decoder network, named SalsaNet, for efficient semantic segmentation of 3D LiDAR point clouds. SalsaNet segments the road, i.e. drivable free-space, and vehicles in the scene by employing the Bird-Eye-View (BEV) image projection of the point cloud. To overcome the lack of annotated point cloud data, in particular for the road segments, we introduce an auto-labeling process which transfers automatically generated labels from the camera to LiDAR. We also explore the role of imagelike projection of LiDAR data in semantic segmentation by comparing BEV with spherical-front-view projection and show that SalsaNet is projection-agnostic. We perform quantitative and qualitative evaluations on the KITTI dataset, which demonstrate that the proposed SalsaNet outperforms other state-of-the-art semantic segmentation networks in terms of accuracy and computation time. Our code and data are publicly available at https://gitlab.com/aksoyeren/salsanet.git