Saint John Baptistery in Florence (Italy): Studies for Conservation of the External Marble Cladding

Saint John’s Baptistery in Florence (Italy), dating back to the XI century, represents one of the most outstanding historical buildings in the city, and has been under the UNESCO patronage as Cultural Heritage since 1982. In recent years, in the frame of a conservation project, detailed studies and mapping of all the tiles covering the Baptistery have been developed. Based on a laser-scan survey, a detailed wireframe model of all the external sides, reporting all the tiles and decor, has been developed. This model was implemented into a 2D-GIS, georeferenced in real scale and spatial position. An in situ survey of all the tiles, ashlars, inlays and columns, made in contradiction by experts in historical ornamental stones, allowed the recognition of several types of marble in place. All these marbles have been analyzed and characterized as geometric, geological and historical data, and the information implemented into a GIS for obtaining a spatial geodatabase representing a “box” to store all information achieved. All these data are manageable by web through smartphone, tablet and PC for querying or updating, thus representing an effective management tool for further conservation of such important historical cultural buildings.

Efficient Calculation Method for Tree Stem Traits from Large-Scale Point Clouds of Forest Stands

With the use of terrestrial laser scanning (TLS) in forest stands, surveys are now equipped to obtain dense point cloud data. However, the data range, i.e., the number of points, often reaches the billions or even higher, exceeding random access memory (RAM) limits on common computers. Moreover, the processing time often also extends beyond acceptable processing lengths. Thus, in this paper, we present a new method of efficiently extracting stem traits from huge point cloud data obtained by TLS, without subdividing or downsampling the point clouds. In this method, each point cloud is converted into a wireframe model by connecting neighboring points on the same continuous surface, and three-dimensional points on stems are resampled as cross-sectional points of the wireframe model in an out-of-core manner. Since the data size of the section points is much smaller than the original point clouds, stem traits can be calculated from the section points on a common computer. With the study method, 1381 tree stems were calculated from 3.6 billion points in ~20 min on a common computer. To evaluate the accuracy of this method, eight targeted trees were cut down and sliced at 1-m intervals; actual stem traits were then compared to those calculated from point clouds. The experimental results showed that the efficiency and accuracy of the proposed method are sufficient for practical use in various fields, including forest management and forest research.

ROADSIDE TREE EXTRACTION AND DIAMETER ESTIMATION WITH MMS LIDAR BY USING POINT-CLOUD IMAGE

Efficient management of roadside trees for local governments is important. Mobile Mapping System (MMS) equipped with a high-density LiDAR scanner has the possibility to be applied to estimate DBH of roadside trees using point clouds. In this study, we propose a method for detecting roadside trees and estimating their DBHs automatically from MMS point clouds. In our method, point clouds captured using the MMS are mapped on a 2D image plane, and they are converted into a wireframe model by connecting adjacent points. Then, geometric features are calculated for each point in the wireframe model. Tree points are detected using a machine learning technique. The DBH of each tree is calculated using vertically aligned circles extracted from the wireframe model. Our method allows robustly calculating the DBH even if there is a hump at breast height. We evaluated our method using actual MMS data measured in an urban area in Tokyo. Our method achieved a high extraction performance of 100 percent of precision and 95.1 percent of recall for 102 roadside trees. The average accuracy of the DBH was 2.0 cm. These results indicate that our method is useful for the efficient management of roadside trees.

Construction of 3D Wireframe Model in Problem of Converting Paper Drawing and Design Documentation into an Electronic Model of an Object

This work is devoted to various stages of the formation of a three-dimensional wireframe model when solving the problem of converting paper drawings into drawing-design documentation. The stages of obtaining geometric-graphic information by an application program and forming a three-dimensional skeleton model are considered in detail. The study of the temporal characteristics of the developed algorithm is also given.

The problem of ambiguity in the task of synthesizing a wireframe model objects according to a technical drawing

The level of quality of the wireframe model obtained in the first stage of solving the problem of restoring the image of a non-planar object depends on the completeness of using the classes of graphic information used to synthesize a 3D model according to the technical drawing. In cases where the drawing is quite simple and allows you to read complete information about the object by existing procedures of automatic "reading the drawing", it is possible to restore the frame models that are fully consistent with the synthesized object. They can be used immediately for further transformations to other types of 3D models. Such frame models are characterized by the fact that all the vertices and edges included in their structure belong to the surface of the desired object. But in most practical cases, when the simulated objects correspond to real products and their technical drawing is full of difficult to formalize information, in the structure of frame models built according to standard procedures, due to a number of objective reasons, there are false geometric elements, i.e. those that are not on the surface of the desired 3D object. This leads to the ambiguity of the restoration of a unique wireframe model corresponding to the object specified in the technical drawing, and requires the development of procedures to avoid the multiplicity of solutions.

Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors

We present a method to infer 3D pose and shape of vehicles from a single image. To tackle this ill-posed problem, we optimize two-scale projection consistency between the generated 3D hypotheses and their 2D pseudo-measurements. Specifically, we use a morphable wireframe model to generate a fine-scaled representation of vehicle shape and pose. To reduce its sensitivity to 2D landmarks, we jointly model the 3D bounding box as a coarse representation which improves robustness. We also integrate three task priors, including unsupervised monocular depth, a ground plane constraint as well as vehicle shape priors, with forward projection errors into an overall energy function.