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With the rapid development of 3D technology, the demand to use and retrieve 3D models has become increasingly urgent. In this paper, we present a framework that consists of a sketch-based local binary pattern (SBLBP) feature extraction method, a learning algorithm for the best view of a shape based on multilayer perceptrons (MLPs) and a learning method for shape retrieval based on two Siamese MLP networks. The model is first projected into many multiview images. A transfer learning scheme based on graphic traversal to identify Harris key points is proposed to build relations between view images and sketches. In addition, an MLP classifier is used for classification to obtain the best views of each model. Moreover, we propose a new learning method for shape retrieval that simultaneously uses two Siamese MLP networks to learn SBLBP features. Furthermore, we build a joint Bayesian method to fuse the outputs of the views and sketches. Based on training with many samples, the MLP parameters are effectively fit to perform shape retrieval. Finally, an experiment is conducted to verify the feasibility of the approach, and the results show that the proposed framework is superior to other approaches.

Semantics-guided lightweighting is proposed to reduce the amount of data processing in the front end by removing the redundant data and creating an IFC Lightweight Scene Graph (IFC_LSG).Double-Layered Sparse Voxel (DLSV) is proposed for data indexing to improve the access efficiency of real-time Web3D building visualization.Incremental Frustum of Interest (I-FOI) is proposed to manage the scene by combining the rendering pipeline and the current scene index. Display Omitted As a result of informatization in construction, Building Information Modeling (BIM) has now become a core technology for smart construction. We present a Web3D-based lightweighting solution for real-time visualization of large-scale BIM scenes, considering the redundancy, semantics, and the parameterization of BIM data under the limited resources of network bandwidth and web browsers. Taking the Industry Foundation Classes (IFC) as the input data format, we firstly conduct a semantics-guided lightweighting operation on the raw BIM scenes by removing the repetitive objects and parameterizing the swept surfaces. Secondly, we extract the exterior products from the raw BIM buildings for visibility culling and construct a Double-Layered Sparse Voxel (DLSV) index based on sparse voxelization. Thirdly, we integrate the above two together into a new data structure named Incremental Frustum of Interest (I-FOI) to manage the scene data in real-time. Our experiments demonstrate that: (1) with the semantics information, our method is able to significantly reduce the redundancy of raw large-scale BIM scenes; (2) the DLSV structure supports progressive data loading and facilitates the indoor/outdoor visibility culling efficiently; and (3) the I-FOI introduces a frustum incremental-driven mechanism into progressive data loading or unloading to improve the efficiency of resource consumption.

We introduce a novel 3D tree skeletonization method from multiple images.Affine transformation and iterative tracking are added to PyrLK optical flow.Parallel voxel flooding is proposed for determining point cloud of single branch.L-System extraction algorithm for tree skeletons is proposed. Display Omitted 3D tree models are widely applied to construct large-scale virtual scenes. However, converting real trees into computer representation faces two main problems. One is the low quality of reconstructed point cloud. The other is that the skeletonization produces inaccurate results due to the complex structure of trees. We propose a novel pipeline to reconstruct automatically accurate 3D tree skeletons from sequential tree images. It involves three steps: (1) Pre-processing: an optical flow based feature-matching algorithm is proposed to acquire high-quality point clouds. (2) Shape-based parallel skeletonization: tree shape is taken into consideration to detect specific structure features of trees during parallel flooding. (3) Post-processing: an L-System extraction algorithm is applied on the skeletons and the lightweight representation is obtained to meet various requirements. The effectiveness of our approach is demonstrated through experiments.

Recent technical progress on the Internet and virtual reality has enabled the proliferation of the applications of distributed virtual environments (DVEs). In a DVE, high-resolution 3D contents may generate huge data while peer-to-peer (P2P) streaming takes advantages to carry these huge traffic in a cost-effective manner. In this P2P paradigm, peers can cache and share DVE data cooperatively to reduce server workload and improve streaming quality. Nevertheless, it is critical to maintain and update the cached contents in each peer efficiently. In this paper, we propose an efficient caching algorithm for a P2P 3D content streaming framework. The proposed caching algorithm is based on a new preservation metric that is defined for balancing visual saliency, reusability and potential relevance of cached 3D objects. Then, these cached 3D objects in each peer are updated adaptively with the ascendant order in importance quantified using this new metric. We implemented the proposed caching algorithm in a simulated DVE platform for P2P-based 3D streaming. We conducted a comprehensive simulation study and our experimental results demonstrate that the proposed peer-to-peer streaming method outperforms the classic 3D streaming methods (including FLoD and MRM) in terms of fill ratio, base latency, requests by nodes and requests to the server.