Predictive 3D streaming
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Multi-Resolution 3D Human Brain Visualization through Streaming
Vani V1, Pradeep Kumar R2 and Mohan S3
1 Department of Information Technology, Dr.N.G.P.IT, Affiliated to Anna University, Coimbatore, India.2 Department of Computer Science Engineering, Adithya IT, Affiliated to Anna University, Coimbatore, India.3 Department of Computer Science Engineering, Dr.N.G.P. IT, Affiliated to Anna University, Coimbatore, India.
Abstract. The well established techniques for video and audio streaming have become the primary inspiration to focus our research towards 3D Streaming. In 3D streaming, the model is buffered and rendered instead of complete download. The central idea of this paper is to visualize the typical 3D human brain model at different resolutions based on the clients requirement at a rapid rate with necessary interactions. This is achieved by initially streaming the simplified version of 3D human brain and then the refinements are streamed based on the clients requirement. The experiment results and analysis affirms the fact that this type of multi resolution streaming reduces the initial time to present the rendered 3D model to the requested clients. Experiments are further conducted to compare the quality of the multi resolution models that are being rendered with respect to the original model.
Keywords: Multi-Resolution model, 3D Streaming, 3D Rendering, 3D Visualization
1 IntroductionIn recent years, 3D real time computer graphics  over the web has given rise to the development of 3D Virtual Environments (VE) . The 3D virtual environment which comprises of set of 3D scenes and the scene in turn consists of set of 3D complex meshes is available on the web and accessed by multiple users at the same time. Therefore, an efficient mode of transmitting the 3D complex meshes across the web has become the need of the hour. In order to cater to the need, the streaming  approach which is promising to deliver the content as per the client request has evolved and few attempts are already made by the researchers. However, in this paper, an attempt is made specifically to stream and deliver the 3D human brain with the required quality requested by the client who could be a medical student/practitioner who wants to visualize the 3D human brain and study in detail about its anatomy by interacting with it. The system he uses to stream and render the 3D human brain may have limited buffering capacity from few KBs to MBs. Mohan S. et al. (Eds.): Proceedings of the International Conference ICSIP 2012, , pp. springerlink.com Springer India 2012
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Therefore, based on the buffering limits of the client, the corresponding LODs can be streamed from server to the client. Also, in order to ensure the quality of the rendered 3D mesh compared with the original mesh a detailed experiment is conducted and results are analyzed. In the process of developing a 3D visualization of human brain with required resolution through steaming firstly, Geometric Level Of Details (GLOD)  library which was developed on top of OpenGL is used at the server end. The GLOD, which focuses on the geometry details of the mesh, has included polygon reduction technique  to reduce the level of details from the original mesh and gives different levels of meshes by reduction process. This process is carried out and required levels of multi resolution meshes are identified and stored in the server for a specific 3D human brain scene which has got only one complex 3D mesh. Secondly at the server end, the quality of the derived LODs with respect to the original mesh (LOD14) is also estimated. Finally the real time streaming is implemented with serialization and de-serialization technique and results are analyzed. In streaming, if the requested scene is the 3D human brain then the initial LOD (Level 1) with very less geometrical information is streamed by computing the total space required and the time spent for streaming. Then, if the total space estimated is available, the streamed data is buffered and rendered at the client end with the rendering time estimated. On the client end, the client can interact (move, rotate, zoom in/out) with the rendered LOD with the help of keyboard and pointing device. Also, either based on the special key press (+ / -) or based on the Euclidean distance between updated camera position and a random point taken from the screen space next/previous LOD would be streamed and rendered if the space criteria is satisfied. This process is repeated by ensuring that the levels remain between 1 and 14.The rest of the paper is organized as follows. In Section 2, we present the overview of Multi resolution 3D model. Section 3 describes the distance measures used to check the quality of the original and reduced multi-resolution mesh. In Section 4, with the 3D human brain model being considered as a specific case, we discussed the proposed work which includes the streaming and 3D visualization process. In Section 5, we discussed the experiment results and analysis. Section 6 concludes by reflecting on the insights we gained from our model and its implications.
2 Multi-Resolution 3D Model An Overview
Highly detailed geometric models are necessary to satisfy the demands of user in 3D virtual world for realism in computer graphics . But, due to the increasing complexity of the models, it makes the virtual world developers feel expensive to store, transmit, and render requested 3D data by the client. In order to store, transmit and render the complex models at the rapid rate, the multi-resolution models  are developed whenever, a representation at low resolution is adequate to meet the needs of the application. Multi-resolution models offer the possibility to manipulate representations of objects at different levels of detail (LOD) [5, 6, 7] and accuracy, depending on the needs of each specific application. In addition to the benefits of speeding up visualization, multi-resolution techniques also support the interactive modeling of detailed objects.
Multi-resolution 3D visualization2
2.1 Types of Hierarchies
The level of detail pipeline consists of three basic stages : geometricsimplification,adaptation, andrendering. The simplification process takes in flat geometry and produces a multi-resolutionhierarchy [5, 7] from it. There are several types of multi-resolution hierarchies, the classification of hierarchies as 1) Discrete 2) Continuous 3) View-dependent or 4) Hierarchical is based on the multi resolution granularity as shown in Fig.1.1) Discretehierarchies (Discrete Level of Detail (DLOD)): refers to the creation of several static levels of detail [5, 7] which are swapped out directly for each other and it is analogous to mip-maps for texturing.
2) Discrete hierarchies are extremely computation efficient as far as rendering is concerned. The DLOD is the simplest and most common form of simplification hierarchy.
It encodes multiple LODs at very coarse granularity. In this, each successive LOD would be half the complexity of the predecessors and the choice of granularity doubles the storage requirements with respect to higher resolution model. Each LOD may also contain the error value indicating the fidelity/quality of it. This kind of simplification is unaware of viewing directions since, the LODs are estimated offline during preprocessing. Therefore, it uniformly reduces object detail. Thus, Discrete LODs are also referred to as isotropic or view-independent LODs
The benefits of DLOD are described as below: LODs are easily compiled into an optimized form for efficient rendering with the help of processes such as Triangular strip reconstruction Re-ordering of vertices based on vertex cache size Indexed vertex arrays
The management of one or more discrete hierarchies is not too computationally expensive within the interactive application (i.e.,) with the given circumstances for each hierarchy appropriate DLOD can be chosen DLODs are most useful when the data comprises of one or more objects that are relatively small in spatial extent. In this case, single choice of resolution is appropriate for the entire model. DLOD is used by a class of models ranging from 3D scanned objects to that of virtual environments.3) Continuoushierarchies (Continuous Level of Detail (CLOD)): creates a progressive data structure [5, 7] from which it can extract a continuous spectrum of detail at run time and it is also referred as dynamic levels of detail. Though it is more computationally complex during rendering, it addresses many of the limitations of DLOD.The benefits of CLOD are described as below: It allows more exact choice of number of primitives to use for an object It enables more subtle transition between LOD It provides convenient representation for progressive transmission of data.
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Since it allows only linear progression and not selective refinement, it is used for same class models as that of DLODs
4) View dependent LOD extends CLOD by creating a hierarchical data structure [5, 7] from which it extracts a mesh tailored to the given viewpoint. . The benefit of the view-dependent hierarchy is overwhelming though there is a run time overhead in adjusting the detail across the model on the fly. 5) Hierarchical Level of Detail (HLOD) [5, 9 and 10] works at scene level with multiple objects and has combined discrete hierarchy with view dependent hierarchy. This LOD hierarchy is more coarse-grained as compared to a typical view-dependent hierarchy and roughly corresponds to the notion of hierarchical scene structure. This coarse-grained representation is beneficial to visualize complex scenes with large number of objects, reducing per object overhead for given ren