Ming and Qing Dynasty Official-Style Architecture Roof Types Classification Based on the 3D Point Cloud

The Ming and Qing Dynasty type of official-style architecture roof can provide plenty of prior knowledge relating to the structure and size of these works of architecture, and plays an important role in the fields of 3D modeling, semantic recognition and culture inheriting. In this paper, we take the 3D point cloud as the data source, and an automatic classification method for the roof type of Ming and Qing Dynasty official-style architecture based on the hierarchical semantic network is illustrated. To classify the roofs into the correct categories, the characteristics of different roof types are analyzed and features including SoRs, DfFtR, DoPP and NoREs are first selected; subsequently, the corresponding feature extraction methods are proposed; thirdly, aiming at the structure of the ridges, a matching graph relying on the attributed relational graph of the ridges is given; based on the former work, a hierarchical semantic network is proposed and the thresholds are determined with the help of the construction rules of the Ming and Qing Dynasty official-style architecture. In order to fully verify the efficiency of our proposed method, various types of Ming and Qing Dynasty official-style architecture roof are identified, and the experimental results show that all structures are classified correctly.

Distributed temporal graph analytics with GRADOOP

Temporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop, a graph dataflow system for scalable, distributed analytics of temporal property graphs which has been continuously developed since 2005. Its graph model TPGM allows bitemporal modeling not only of vertices and edges but also of graph collections. A declarative analytical language called GrALa allows analysts to flexibly define analytical graph workflows by composing different operators that support temporal graph analysis. Built on a distributed dataflow system, large temporal graphs can be processed on a shared-nothing cluster. We present the system architecture of Gradoop, its data model TPGM with composable temporal graph operators, like snapshot, difference, pattern matching, graph grouping and several implementation details. We evaluate the performance and scalability of selected operators and a composed workflow for synthetic and real-world temporal graphs with up to 283 M vertices and 1.8 B edges, and a graph lifetime of about 8 years with up to 20 M new edges per year. We also reflect on lessons learned from the Gradoop effort.

OBJECT DETECTION AND CLASSIFICATION FROM CLUTTERED LARGE-SCALE INDOOR SCENE VIA ANCHOR-BASED GRAPH

Indoor object detection and classification from scanned point clouds has recently attracted considerable research interest. However, detecting and classifying objects with arbitrary upward orientation has emerged as a substantial challenge. This paper presents an anchor-based graph method via geometric and topological similarity among indoor objects. With this method, the misclassification that usually occurs in the objects placed non-vertical with the floor is overcome by extracting anchor in each graph via nodes’ geometric attribute and by matching graph via topological relationship between nodes and anchor, rather than the features along the upward orientation. A region growing-based method along the anchor’s upward orientation is proposed for classifying the unlabeled over-segmentation parts. Such an anchor-based method ensures both the accuracy of object classification and the geometric integrity of object. A series of experimental tests using three real-world 3D scans of indoor environments show the effectiveness and feasibility of the proposed method.

A semi-automatic tool to georeference historical landscape images

Smapshot is a web-based participatory virtual globe where users can georeference historical images of the landscape by clicking a minimum of six well identifiable correspondence points between the image and a 3D virtual globe. The images database is expected to grow exponentially. In a near future, the work of the web users will no longer be enough. To tackle this issue, we developed a semi-automatic process to georeference images. The volunteers will be shown only images having a maximum number of neighbour images in the matching graph. These neighbour images are the ones with which they share some overlay. This overlap is detected using the SIFT algorithm in a pairewise matching process. For an image pair made of a reference image with a known pose and a query image we want to georeference, we extracted the 3D world coordinates of the tie points from a digital elevation model. Then, by running a perspective-n-point algorithm after having geometrically tested the resulting homography between the two images, we compute the 6 degree of freedom pose, i.e. the position (X,Y,Z) and orientation (azimuth, tilt and roll angles) of the query image. The query image then becomes a reference and the georeference computation can be propagated more deeply in the graph structure.

A semi-automatic tool to georeference historical landscape images

Smapshot is a web-based participatory virtual globe where users can georeference historical images of the landscape by clicking a minimum of six well identifiable correspondence points between the image and a 3D virtual globe. The images database is expected to grow exponentially. In a near future, the work of the web users will no longer be enough. To tackle this issue, we developed a semi-automatic process to georeference images. The volunteers will be shown only images having a maximum number of neighbour images in the matching graph. These neighbour images are the ones with which they share some overlay. This overlap is detected using the SIFT algorithm in a pairewise matching process. For an image pair made of a reference image with a known pose and a query image we want to georeference, we extracted the 3D world coordinates of the tie points from a digital elevation model. Then, by running a perspective-n-point algorithm after having geometrically tested the resulting homography between the two images, we compute the 6 degree of freedom pose, i.e. the position (X,Y,Z) and orientation (azimuth, tilt and roll angles) of the query image. The query image then becomes a reference and the georeference computation can be propagated more deeply in the graph structure.