Augmented Reality (AR) is to be used extensively in today's digitized teaching in order to enable students to a more efficient learning. However, teaching content must not be only digitised, but must be communicated in a meaningful way. For this purpose, a generally valid flowchart has been developed, that allows lectures to choose the right content for AR experiences. In order to create this flowchart, empirical values from test runs were combined with pedagogically proven empirical values for good teaching.

/pdf/reasonable-application-of-augmented-reality-in-engineering-1vmr8c9i06.pdf

Reasonable application of augmented reality in engineering education

这份报纸为多角形的模型论述一个新奇几何 voxelization 算法。首先，距离计算是由为线 / 表面 voxelization 处理在几何原语和 voxels 之间的单位(GPU ) 的图形上的片的执行的片。一个新奇稳固的充满过程然后被建议帮助表面 voxelization 并且完成稳固的 voxelization。而且用建议转移函数，二进制代码和抗混叠 voxelizations 是可完成的。最后，建议途径能被用于 voxelize 用线 voxelization 为 3D 向量地优化。建议途径获得需要的试验性的结果。

Novel Geometrical Voxelization Approach with Application to Streamlines

Point cloud decomposition by internal and external critical points

Decomposing a complex object into simple components is a fundamental problem in geometry processing. Existing methods for decomposing point clouds rely on local or global features of an object, which leads to over-segmentation or unnatural component boundaries. In this paper, we propose a novel method for decomposing the point cloud by using internal and external critical points. First, we propose a novel shrinking strategy to build the global skeleton topology, from which we can extract internal critical points for locating components. External critical points are selected from the ridge and valley points for component segmentation. Then, we apply the constraint of internal and external critical points to decompose an object into semantic components by skeleton-based piecewise labeling. Experimental results demonstrate that our method is effective in decomposing 3D point clouds and is robust to limited noise and incomplete data. • A shrinking-based skeleton extraction method is proposed to reserve sharp features. • A point cloud decomposition based on global and local features is presented. • Our framework has good efficiency and robustness in point cloud decomposition. 

Segmentation of 3D point clouds is vigorously discussed in recent years. Many existing techniques pre-process the data to identify critical points for meaningful features. Very often, the critical points are predicted at curvature objects and this does not always generate promising outcome. This paper proposes to segment the point clouds based on its skeleton as this robustly reflects the global shape of an object. The skeleton is constructed via Laplacian-based contraction method. Spherical approach is applied along the skeleton to segment the point clouds. The entire process is automatic. Only moderate user-input is applied to align the skeleton to the object. The output of the proposed method is to be compared with another popular segmentation method with critical points input. The result shows that the proposed method generates more accurate segmented features.

Semi-automatic segmentation of 3d point clouds skeleton without explicit computation for critical points

Creating a marker based Augmented Reality (AR) usually requires a series of files such as marker files and 3d model files. The series of files that Marker Based Augmented Reality requires will limit its capability and reduces its reliability. These files and resources have to be loaded from storage such as a local machine or a web server. We propose a Resource Independent Marker Based Augmented Reality (RIMBAR) by encoding resources such as 3D model files as QR code and using the QR code itself as the marker. The AR system does not need any marker file or 3d model file. We processed, shortened and convert the content to make it fit into a QR code. Larger contents are then split to multiple markers and the data is joined together at the other end. Currently this system shows potential but further research needs to be done to remove the issues.

/pdf/a-resource-independent-marker-based-augmented-reality-2v1fme015v.pdf

A Resource-Independent Marker-Based Augmented Reality Application

Three-dimensional (3D) point cloud model presents limited geometric detail. Many existing research works have to apply Nearest-Neighbor (NN) search technique to implicitly find the closest appropriate point to form regular triangle mesh, in order to acquire more geometric detail (e.g. vectors, area and etc.) to define the model. This paper introduces a volume-based clustering technique to group the associated points in different regions. The region can adaptively be partitioned based on the density of the points. Each region will be consigned a key point to represent all its members. This can easily control the complexity of the model and alleviate the computational process. In our implementation, we further extend this to layer peeling which can reveal more of the inner structure of a model for other research application. We have tested on three models and produce different layers of peeling. Each gives unique and interesting region representation. This piece of work is significance for beginners who would deal with high density of 3D point cloud models.

/pdf/layer-peeling-through-volume-based-clustering-in-3d-point-19admnk6e0.pdf

Layer peeling through volume-based clustering in 3d point cloud models

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Papers

Semi-automatic segmentation of 3d point clouds skeleton without explicit computation for critical points

A Resource-Independent Marker-Based Augmented Reality Application

Layer peeling through volume-based clustering in 3d point cloud models

A Resource-Independent Marker-Based Augmented Reality Application