基于时空连贯性和几何简化技术的复杂场景快速消隐绘制算法

提出了一个结合层次遮挡图像缓存的快速消隐绘制算法,本算法首先利用空间连贯性对场景实行快速保守的消隐,对可能可见的近景、中景使用几何绘制,对可能可见的远景实现了基于图像和几何混合的加速绘制,实验表明,由于充分利用了空间连贯性和图像简化技术,本算法效果良好,可适合各种复杂度场景的快速绘制。     

Output-SensitiveVisibility Algorithms for Dynamic Scenes with Applications to Virtual Reality

An output-sensitive visibility algorithm is one whose runtime is proportional to the number of visible graphic primitives in a scene model—not to the total number of primitives, which can be much greater. The known practical output-sensitive visibility algorithms are suitable only for static scenes, because they include a heavy preprocessing stage that constructs a spatial data structure which relies on the model objects’ positions. Any changes to the scene geometry might cause significant modifications to this data structure. We show how these algorithms may be adapted to dynamic scenes. Two main ideas are used: first, update the spatial data structure to reflect the dynamic objects’ current positions; make this update efficient by restricting it to a small part of the data structure. Second, use temporal bounding volumes (TBVs) to avoid having to consider every dynamic object in each frame. The combination of these techniques yields efficient, output-sensitive visibility algorithms for scenes with multiple dynamic objects. The performance of our methods is shown to be significantly better than previous output-sensitive algorithms, intended for static scenes. TBVs can be adapted to applications where no prior knowledge of the objects’ trajectories is available, such as virtual reality (VR), simulations etc. Furthermore, they save updates of the scene model itself; notjust of the auxiliary data structure used by the visibility algorithm. They can therefore be used to greatly reduce the communications overhead in client-server VR systems, as well as in general distributed virtual environments.     

Environmental Objects for Authoring Procedural Scenes

We propose a novel approach for authoring large scenes with automatic enhancement of objects to create geometric decoration details such as snow cover, icicles, fallen leaves, grass tufts or even trash. We introduce environmental objects that extend an input object geometry with a set of procedural effects that defines how the object reacts to the environment, and by a set of scalar fields that defines the influence of the object over of the environment. The user controls the scene by modifying environmental variables, such as temperature or humidity fields. The scene definition is hierarchical: objects can be grouped and their behaviours can be set at each level of the hierarchy. Our per object definition allows us to optimize and accelerate the effects computation, which also enables us to generate large scenes with many geometric details at a very high level of detail. In our implementation, a complex urban scene of 10 000 m², represented with details of less than 1 cm, can be locally modified and entirely regenerated in a few seconds.     

运用改进的八叉树算法实现精确碰撞检测

提出一种精确碰撞检测算法,通过计算空间多面体之间距离实现碰撞检测功能.在计算2个多面体之间距离时,运用空间层次划分技术高效地寻找多面体中充分接近的三角面片,然后在这些三角面片中进行距离计算,以提高算法效率;同时运用改进的八叉树层次分割算法,与基本八叉树算法相比,减少了算法的空间复杂度.文中算法已经在超导Tokamak实验装置（EAST）虚拟装配仿真系统的碰撞检测模块中得到应用,通过实验比较,证明了该算法的可行性.     

Streaming of Complex 3D Scenes for Remote Walkthroughs

We describe a new 3D scene streaming approach for remote walkthroughs. In a remote walkthrough, a user on a client machine interactively navigates through a scene that resides on a remote server. Our approach allows a user to walk through a remote 3D scene, without ever having to download the entire scene from the server. Our algorithm achieves this by selectively transmitting only small parts of the scene and lower quality representations of objects, based on the user's viewing parameters and the available connection bandwidth. An online optimization algorithm selects which object representations to send, based on the integral of a benefit measure along the predicted path of movement. The rendering quality at the client depends on the available bandwidth, but practical navigation of the scene is possible even when bandwidth is low.     

虚拟景区中层次碰撞检测方法的研究

针对虚拟景区场景大、对象多的特点,提出了一种层次碰撞检测方法。首先运用空间剖分法过滤掉远处不可能碰撞的对象,然后再通过构建碰撞检测空间,剔除掉待测运动对象附近暂时不可能碰撞的对象,最后再运用S-AABB层次包围盒法根据每个对象的具体精度要求进行精确碰撞检测。该碰撞检测方法在满足实际精度要求的前提下,降低了碰撞检测次数和复杂度,提高了实时性,是一种针对虚拟场景高效快速的碰撞检测方法。     

A multi-temporal masking classification method for vineyard monitoring in central Spain

Abstract

The objective of image segmentation in remote sensing is to define regions in an image that correspond to objects in the ground scene. Traditional scene models underlying image segmentation procedures have assumed that objects as manifest in images have internal variances that are both low and equal. This scene model is unrealistically simple. An alternative scene model recognizes different scales of objects in scenes. Each level in the hierarchy is nested, or composed of objects or categories of objects from the preceding level. Different objects may have distinct attributes, allowing for relaxation of assumptions like equal variance.

A multiple-pass, region-based segmentation algorithm improves the segmentation of images from scenes better modelled as a nested hierarchy. A multiple-pass approach allows slow and careful growth of regions while inter-region distances are below a global threshold. Past the global threshold, a minimum region size parameter forces development of regions in areas of high local variance. Maximum and viable region size parameters limit the development of undesirably large regions.

Application of the segmentation algorithm for forest stand delineation in Landsat TM imagery yields regions corresponding to identifiable features in the landscape. The use of a local variance, adaptive-window texture channel in conjunction with spectral bands improves the ability to define regions corresponding to sparsely-stocked forest stands which have high internal variance.     

Indoor scene recognition by a mobile robot through adaptive object detection

Mobile robotics has achieved notable progress, however, to increase the complexity of the tasks that mobile robots can perform in natural environments, we need to provide them with a greater semantic understanding of their surrounding. In particular, identifying indoor scenes, such as an Office or a Kitchen, is a highly valuable perceptual ability for an indoor mobile robot, and in this paper we propose a new technique to achieve this goal. As a distinguishing feature, we use common objects, such as Doors or furniture, as a key intermediate representation to recognize indoor scenes. We frame our method as a generative probabilistic hierarchical model, where we use object category classifiers to associate low-level visual features to objects, and contextual relations to associate objects to scenes. The inherent semantic interpretation of common objects allows us to use rich sources of online data to populate the probabilistic terms of our model. In contrast to alternative computer vision based methods, we boost performance by exploiting the embedded and dynamic nature of a mobile robot. In particular, we increase detection accuracy and efficiency by using a 3D range sensor that allows us to implement a focus of attention mechanism based on geometric and structural information. Furthermore, we use concepts from information theory to propose an adaptive scheme that limits computational load by selectively guiding the search for informative objects. The operation of this scheme is facilitated by the dynamic nature of a mobile robot that is constantly changing its field of view. We test our approach using real data captured by a mobile robot navigating in Office and home environments. Our results indicate that the proposed approach outperforms several state-of-the-art techniques for scene recognition.     

Visual immersive haptic mathematics

In the modern urban society, human brain is not being sufficiently trained to deal with problems which require 3D perception. As a result, when teaching subjects richly infused with mathematics it is usually a challenge for the learners to follow the instructor and visualize how mathematical concepts reflect in 3D geometry and colors. We have proposed an approach that would allow for defining complex geometry, visual appearance and tangible physical properties of the virtual objects using language of mathematical functions. It allows the learners to get immersed within the 3D scene and explore the shapes which are being modeled visually and haptically. We illustrate this concept using our function-based extension of X3D and VRML. Besides definition of objects with mathematical functions straight in the scene file, standard X3D and VRML objects can be converted to tangible ones as well as augmented with function-defined visual appearances. Since the function-defined models are small in size, it is possible to perform their collaborative interactive modifications with concurrent synchronous visualization at each client computer with any required level of detail.     

An algorithm for LOD by merging near coplanar faces based on gauss sphere

LOD（Level of Detail)models are widely used recently to accelerate the rendering of 3D scenes.An algorithm that creates multiple levels of detail for 3D scene y merging near-coplanar faces is presented in this paper,First a Gauss sphere is defined for the model of scene and it is divided into meshes near-uniformly.Then,the faces of objects are attached to the respective spherical meshes according to their normal direction.If faces attached to the same mesh are connected with each other,they are merged to form a near coplanar patch (Superface),Isolated vertices inside the patch are removed and the patch is retriangulated.To further imporve the simplification,vicinity vertices on the boundary of the surface patch are merged.In the algorithm,a planar separate rule planar-enneatree is adopted to set up a hierarchical structure of the Gauss sphere,which is used to support the hierarchical model of the scene(LOD),the experimental result shows that the algorithm can achieve desired simplification effects.     

基于多线程并行的大规模场景交互漫游研究

针对大规模三维场景的交互漫游,提出了一种基于多线程并行调度解决方案,并给出了相应的交互漫游算法.该方法使用离散层次细节技术结合视点相关的动态连续层次细节选择和过渡的批LOD技术.在预处理阶段,对大规模场景进行分层分块处理;在实时漫游阶段,采用多线程并行技术:绘制线程利用四又树层次进行可见性剔除和视点相关简化获取当前可绘地形,并将其提交给GPU进行绘制.预取线程通过预测视点的位置,从外存预取相关的几何数据并调入内存.将该方法应用于具体实例,取得了良好效果,证明了该并行方法的有效性.     

虚拟环境中的混合层次包围盒算法研究

针对采用单一层次包围盒进行碰撞检测在实时性和精确性方面的不足,提出基于轴向包围盒(AABB)结构和有向包围盒(OBB)的混合层次包围盒的碰撞检测方法(SHBVs).通过分析各种层次包围盒的特点以及虚拟手术环境中的对象特点,混合层次包围盒的碰撞检测方法将包围盒树分为上下两层,上层采用AABB-AABB的方式,用来快速排除不可能相交的物体;下层采用OBB-AABB的方式,能更紧密的包围虚拟环境中的活动对象(如细长的手术器械),同时对环境对象(软体组织)能更快速地在软组织变形后进行更新.实验证明,提出的混合层次包围盒算法能更快地检测碰撞,达到较好的实时性和精确性.     

Hierarchical structure to winged-edge structure: a conversion algorithm

The winged-edge data structure is advantageous for traversing the topological graph of the boundary representation of a solid object. This paper presents an algorithm for converting hierarchical boundary representations into representations in the winged-edge data structure. As a result of the conversion, the adjacency relationships of geometric entities embedded in hierarchical boundary representations,-which may be evaluated through boundary evaluation on solid objects defined via Boolean set-operations, can be easily and efficiently accessed.     

Invariant object recognition with trace learning and multiple stimuli present during training

Over successive stages, the ventral visual system develops neurons that respond with view, size and position invariance to objects including faces. A major challenge is to explain how invariant representations of individual objects could develop given visual input from environments containing multiple objects. Here we show that the neurons in a 1-layer competitive network learn to represent combinations of three objects simultaneously present during training if the number of objects in the training set is low (e.g. 4), to represent combinations of two objects as the number of objects is increased to for e.g. 10, and to represent individual objects as the number of objects in the training set is increased further to for e.g. 20. We next show that translation invariant representations can be formed even when multiple stimuli are always present during training, by including a temporal trace in the learning rule. Finally, we show that these concepts can be extended to a multi-layer hierarchical network model (VisNet) of the ventral visual system. This approach provides a way to understand how a visual system can, by self-organizing competitive learning, form separate invariant representations of each object even when each object is presented in a scene with multiple other objects present, as in natural visual scenes.     

Conservative Visibility and Strong Occlusion for Viewspace Partitioning of Densely Occluded Scenes

Computing the visibility of out-door scenes is often much harder than of in-door scenes. A typical urban scene, for example, is densely occluded, and it is effective to precompute its visibility space, since from a given point only a small fraction of the scene is visible. The difficulty is that although the majority of objects are hidden, some parts might be visible at a distance in an arbitrary location, and it is not clear how to detect them quickly. In this paper we present a method to partition the viewspace into cells containing a conservative superset of the visible objects. For a given cell the method tests the visibility of all the objects in the scene. For each object it searches for a strong occluder which guarantees that the object is not visible from any point within the cell. We show analytically that in a densely occluded scene, the vast majority of objects are strongly occluded, and the overhead of using conservative visibility (rather than visibility) is small. These results are further supported by our experimental results. We also analyze the cost of the method and discuss its effectiveness.     

Advances in information extraction techniques

The analysis of satellite images may evolve from ad hoc methods of utilizing spatial and temporal context to the application of artificial-intelligence-oriented procedures of hierarchical scene analysis. Pattern representations more abstract than Euclidean vector spaces offer some hope of unifying structural and decision-theoretical approaches. The estimation of expected classification error rates is becoming more sophisticated and rigorous, but useful finite-sample results for nonparametric distributions appear unobtainable. Focus on computational complexity allows comparison of algorithms, while software engineering techniques reduce the effort necessary to develop and maintain complex image processing systems. Advances in computer systems architecture, commercial database technology, and man-machine communications should be closely monitored by the remote sensing community.     

A Hierarchical and Contextual Model for Aerial Image Parsing

In this paper we present a hierarchical and contextual model for aerial image understanding. Our model organizes objects (cars, roofs, roads, trees, parking lots) in aerial scenes into hierarchical groups whose appearances and configurations are determined by statistical constraints (e.g. relative position, relative scale, etc.). Our hierarchy is a non-recursive grammar for objects in aerial images comprised of layers of nodes that can each decompose into a number of different configurations. This allows us to generate and recognize a vast number of scenes with relatively few rules. We present a minimax entropy framework for learning the statistical constraints between objects and show that this learned context allows us to rule out unlikely scene configurations and hallucinate undetected objects during inference. A similar algorithm was proposed for texture synthesis (Zhu et al. in Int. J. Comput. Vis. 2:107–126, 1998) but didn’t incorporate hierarchical information. We use a range of different bottom-up detectors (AdaBoost, TextonBoost, Compositional Boosting (Freund and Schapire in J. Comput. Syst. Sci. 55, 1997; Shotton et al. in Proceedings of the European Conference on Computer Vision, pp. 1–15, 2006; Wu et al. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, 2007)) to propose locations of objects in new aerial images and employ a cluster sampling algorithm (C4 (Porway and Zhu, 2009)) to choose the subset of detections that best explains the image according to our learned prior model. The C4 algorithm can quickly and efficiently switch between alternate competing sub-solutions, for example whether an image patch is better explained by a parking lot with cars or by a building with vents. We also show that our model can predict the locations of objects our detectors missed. We conclude by presenting parsed aerial images and experimental results showing that our cluster sampling and top-down prediction algorithms use the learned contextual cues from our model to improve detection results over traditional bottom-up detectors alone.     

Generating compact representations of static scenes by means of 3D object hierarchies

This paper presents a new heuristic algorithm for computing a compact hierarchical representation of the objects contained in a 3D static scene. The algorithm first builds a fully-connected adjacency graph that keeps the costs of grouping the different pairs of objects of the scene. Afterward, the graph’s minimum spanning tree is computed and its edges sorted in ascending order according to their cost. Next, from that sorted list, a cost-based clustering technique is applied, thus generating new objects at a higher level in the hierarchy. A new object can be defined after merging two or more objects according to their corresponding linking costs. The algorithm starts over by generating a new adjacency graph from those new objects, along with the objects that could not be merged before. The iterative process is applied until an adjacency graph with a single object is obtained. The latter is the root of the hierarchical representation. Balance and coherence of the hierarchy, in which spatially close objects are also structurally close, is achieved by defining an appropriate cost function. The proposed technique is evaluated upon several 3D scenes and compared to a previous technique. In addition, the benefits of the proposed technique with respect to techniques based on octrees and kd-trees are analyzed in terms of a practical application.