Pu Ren

Automatic planar shape segmentation from indoor point clouds

The use of a terrestrial laser scanner (TLS) has become a popular technique for the acquisition of 3D scenes in architecture and design. Surface reconstruction is used to generate a digital model from the acquired point clouds. However, the model often consists of excessive data, limiting real-time user experiences that make use of the model. In this study, we present a coarse to fine planar shape segmentation method for indoor point clouds, which results in the digital model of an indoor scene being represented by a small number of planar patches. First, the Gaussian map and region growing techniques are used to coarsely segment the planar shape from sampled point clouds. Then, the best-fit-plane is calculated by random sample consensus (RANSAC), avoiding the negative impact of outliers. Finally, the refinement of planar shape is produced by projecting point clouds onto the corresponding bestfit-plane. Our method has been demonstrated to be robust towards noise and outliers in the scanned point clouds and overcomes the limitations of over- and under-segmentation. We have tested our system and algorithms on real datasets and experiments show the reliability of the proposed method against existing region-growing methods.

3D scanning modeling method application in ancient city reconstruction

With the development of optical engineering technology, the precision of 3D scanning equipment becomes higher, and its role in 3D modeling is getting more distinctive. This paper proposed a 3D scanning modeling method that has been successfully applied in Chinese ancient city reconstruction. On one hand, for the existing architectures, an improved algorithm based on multiple scanning is adopted. Firstly, two pieces of scanning data were rough rigid registered using spherical displacers and vertex clustering method. Secondly, a global weighted ICP (iterative closest points) method is used to achieve a fine rigid registration. On the other hand, for the buildings which have already disappeared, an exemplar-driven algorithm for rapid modeling was proposed. Based on the 3D scanning technology and the historical data, a system approach was proposed for 3D modeling and virtual display of ancient city.

Manifold Ranking for Sketch-Based 3D Model Retrieval

The demand for 3D model retrieval is increasing, and the sketch-based method has been proven to be the most effective and efficient approach to retrieve 3D models. The existing methods calculate distance based on feature extraction, showing its limitation in improving retrieval accuracy. Thus, a second ranking making use of relevance between features is a good way to go. In this paper, an extended manifold ranking method is presented as a new retrieval framework. Line drawings are abstracted to represent 3D models, and a visual vocabulary is used to describe the local features of both sketches and line drawings. To rank the similarities between models, a method of semantic classification as a constraint is presented. We use similarity weight to control the classification difference between models so that the ranking score of models that belong to the same class holds a higher similarity weight. Furthermore, based on the idea of manifold learning, a KNN algorithm is adopted to obtain better ranking results. Experiments on standard testing datasets have demonstrated that the proposed algorithm significantly improves the accuracy of 3D model retrieval and outperforms current state-of-the-art algorithms by comparison.

A Rapid Modeling Method for 3D Architectural Scene

The existing 3D scene layout methods, which mainly focus on indoor scenes, are limited in outdoor applications. In this paper, the example-based modeling method is introduced into the outdoor modeling, and an automatic layout optimization method for outdoor scenes is proposed. As an application platform, the sketch-based 3D model retrieval and assemble system is realized as well. Different from the current methods, firstly, we adopt an improved manifold sorting algorithm in sketch retrieval method, which can get the 3D models rapidly; secondly, according to particular properties of outdoor architectures, specialized energy constraints are proposed, which defines the energy function that meets the functional and aesthetic needs; thirdly, the scene achieves automatic layout by adopting simulated annealing algorithm. By stepped asymptotic optimization strategy and random jumps, we can avoid falling into constraint conflicts and local optimal traps. Experimental results show the robustness and effectiveness of our algorithm in different scenes. Our algorithm has been applied in the actual development of game scenes.

A Sketch-based Rapid Modeling Method for Crime Scene Presentation

The reconstruction of a crime scene plays an important role in digital forensic application. This article integrates computer graphics, sketch-based retrieval, and virtual reality (VR) techniques to develop a low-cost and rapid 3D crime scene presentation approach, which can be used by investigators to analyze and simulate the criminal process. First , we constructed a collection of 3D models for indoor crime scenes using various popular techniques, including laser scanning, imagebased modeling and geometric modeling. Second, to quickly obtain an object of interest from the 3D model database, a sketch-based retrieval method was proposed. Finally, a rapid modeling system that integrates our database and retrieval algorithm was developed to quickly build a digital crime scene. For practical use, an interactive real-time virtual roaming application was developed in Unity 3D and a low-cost VR head-mounted display (HMD). Practical cases have been implemented to demonstrate the feasibility and availability of our method.

Sketch-Based Modeling and Immersive Display Techniques for Indoor Crime Scene Presentation

The reconstruction of crime scene plays an important role in digital forensic application. Although the 3D scanning technique is popular in general scene reconstruction, it has great limitation in the practice use of crime scene presentation. This article integrates computer graphics, sketch-based modeling and virtual reality (VR) techniques to develop a low-cost and rapid 3D crime scene presentation approach, which can be used by investigators to analyze and simulate the criminal process. First, we constructed a collection of 3D models for indoor crime scenes using various popular techniques, including laser scanning, image-based modeling and software-modeling. Second, to quickly obtain an object of interest from the 3D model database that is consistent with the geometric structure of the real object, a sketch-based retrieval method was proposed. Finally, a rapid modeling system that integrates our database and retrieval algorithm was developed to quickly build a digital crime scene. For practical use, an interactive real-time virtual roaming application was developed in Unity 3D and a low-cost VR head-mounted display (HMD). Practical cases have been implemented to demonstrate the feasibility and availability of our method.

A Smoothed Finite Element-Based Elasticity Model for Soft Bodies

One of the major challenges in mesh-based deformation simulation in computer graphics is to deal with mesh distortion. In this paper, we present a novel mesh-insensitive and softer method for simulating deformable solid bodies under the assumptions of linear elastic mechanics. A face-based strain smoothing method is adopted to alleviate mesh distortion instead of the traditional spatial adaptive smoothing method. Then, we propose a way to combine the strain smoothing method and the corotational method. With this approach, the amplitude and frequency of transient displacements are slightly affected by the distorted mesh. Realistic simulation results are generated under large rotation using a linear elasticity model without adding significant complexity or computational cost to the standard corotational FEM. Meanwhile, softening effect is a by-product of our method.

Rapid three-dimensional scene modeling by sketch retrieval and auto-arrangement

A completed workflow for rapid 3D outdoor scene modeling is implemented.Sketch-based retrieval is improved by manifold ranking obtaining high accuracy.Energy function is composed by specific constraints designing for outdoor scenes.Auto-arrangement is optimized by PSO-SA algorithm efficiently.Effectiveness is proved by evaluations of practical experiments and user study. Display Omitted The existing three-dimensional (3D) object layout methods are focused mainly on indoor scenes and they are limited for outdoor applications. In this study, we propose a data-driven method for outdoor scene modeling by using fast retrieval and automatic optimization layout techniques. Unlike the current methods, we first employ an improved manifold ranking algorithm in the sketch-based 3D model retrieval stage, which achieves higher accuracy. Next, according to the particular properties of outdoor architectures, specialized constraints are then proposed to define an energy function, which meets both the functional and aesthetic requirements. Finally, we cast the auto-arrangement as a combinatorial optimization problem, which we solve using an optimization algorithm. In contrast to the earlier version of this method, which was presented at Cyberworlds 2016, this extended version combines simulated annealing and particle swarm optimization algorithms, which have the advantages of rapid convergence and avoiding becoming trapped by local minima. Our experimental results demonstrate that the proposed method is more intuitive and effective for modeling 3D scenes, and can be employed in the actual development of game scenes.

Data-driven modeling for chinese ancient architectures

Chinese ancient architecture is well known for its unique shape styles and complex constructions, which results in a complicated 3D modeling process. In contrast to the existing procedural modeling method which generates architectures relying on fixed rules, our data-driven approach aggregate information from 3D model collections to realize analysis, inference and synthesis of components spontaneously. For proprietary properties of ancient building models, this paper describes the framework of the complete procedure from shape representation to component assembly. The plausible combinations for structural components are also learned from 3D model dataset by the Bayesian network. As a periodic research, some remaining works at each stage are proposed in this paper. We conclude these works with ideas that can inspire future research in the modeling issue for Chinese ancient architecture.

A Probabilistic Model for Traditional Chinese Architecture

Increasing numbers of 3D models provide a greatopportunity for data-driven shape modeling, analysis andsynthesis. The most critical core technique is to aggregateinformation from model collections to improve reasoningtheir properties and relationships. In this paper, we proposea probabilistic model for traditional Chinese architectures, which encodes the semantic type and hierarchicalrelationships for their basic components. Firstly aprobabilistic hierarchical graph is designed to represent thetypical component structure of Chinese ancient buildings. Secondly, a Bayesian Network is trained from a collection of3D models with consistent labels. Finally, the BayesianNetwork with structure and parameters learned from datacan be used to synthesis and recommend components inapplications. Experimental results show the effective of theproposed method.