Huge texture mapping for real-time visualization of large-scale terrain

A number of 3D digitizing methods, including stereopsis, are capable of measuring not only an object's shape but also its surface texture. Measured shape data can be expressed as a polyhedron whose faces are triangular, and object‐surface texture data can be represented in the form of color data for each of the vertices of the various triangles. The ability to apply object‐surface texture data directly to the creation of computer graphics images has been severely limited by the extreme difficulty of expressing such texture data in the image from which conventional texture mapping proceeds (commonly referred to as a texture map). Proposed here is a method that generates a texture map from object‐surface texture data. First, the method reduces the number of triangles in the polyhedron while preserving essentially all the color data that it originally contained. Next, it arranges the triangles in the simplified triangle mesh onto a plane, and generates a texture map from this arrangement. This method preserves the full texture of an object, no matter how complex its shape, an advantage over the conventional cylindrical texture representation approach. Furthermore, since essentially all color data has been retained, the reduction in the number of triangles does not produce any significant reduction in the texture‐realism of the object image produced.

Differing from common 2D images, a texture map, since it is used to project onto a 3D model in 3D space, not only contains 2D texture information, but also implicitly associates certain 3D geometric information. Related to this, an effective 3D geometry-dependent texture map compression method with hybrid region of interest (ROI) coding is proposed in this paper. We regard the visually important area of the texture map as the ROI. To acquire the visually important areas of the texture map, we take into account information from both the 3D geometry and 2D texture maps, depicting the saliency of the textured model, distortion of the texture mapping, and boundary of the texture atlas. These visually important areas are expressed as a visual importance map. According to the particularity of the texture map, a hybrid ROI coding method that utilizes Max-Shift and an improved post compression rate distortion (PCRD) technique is presented, guided by this visual importance map. To find the exact wavelet coefficients pertaining to these ROIs before carrying out the hybrid ROI coding, this paper proposes a stochastic coefficient priority mask map computational method. Experimental results show that the visually important areas of the texture image have a better visual effect and that a good rendering result can be obtained from the texture mapping.

This article describes a method to develop a generic approach to acquire navigation capabilities for the standard platform of the IMAV indoor competition: the Parrot AR.Drone. Our development is partly based on simulation, which requires both a realistic sensor and motion model. The AR.Drone simulation model is described and validated. Furthermore, this article describes how a visual map of the indoor environment can be made, including the effect of sensor noise. This visual map consists of a texture map and a feature map. The texture map is used for human navigation and the feature map is used by the AR.Drone to localize itself. To do so, a localization method is presented. An experiment demonstrates how well the localization works for circumstances encountered during the IMAV competition.

Automatic Face recognition of people is a challenging problem which has received much attention during the recent years due to its potential multimedia applications in different fields such as 3D videoconference, security applications or video indexing. However, there is no technique that provides a robust solution to all situations and different applications, yet. Face recognition includes a set of challenges like expression variations, occlusions of facial parts, similar identities, resolution of the acquired images, aging of the subjects and many others. Among all these challenges, most of the face recognition techniques have evolved in order to overcome two main problems: illumination and pose variation. Either of these influences can cause serious performance degradation in a 2D face recognition system.Some of the new face recognition strategies tend to overcome both research topics from a 3D perspective. The 3D data points corresponding to the surface of the face may be acquired using different alternatives: A multi camera system (stereoscopy), structured light, range cameras or 3D laser and scanner devices The main advantage of using 3D data is that geometry information does not depend on pose and illumination and therefore the representation of the object does not change with these parameters, making the whole system more robust. However, the main drawback of the majority of 3D face recognition approaches is that they need all the elements of the system to be well calibrated and synchronized to acquire accurate 3D data (texture and depth maps). Moreover, most of them also require the cooperation or collaboration of the subject during a certain period of time. All these requirements can be available during the training stage of many applications. When enrolling a new person in the database, it could be performed off-line, with the help o human interaction and with the cooperation of the subject to be enrolled. On the contrary, the previous conditions are not always available during the test stage. The recognition will be in most of the cases in a semicontrolled or uncontrolled scenario, where the only input of the system will probably consists of a 2D intensity image acquired from a single camera. This leads to a new paradigm where 2D-3D mixed face recognition approaches are used. The idea behind this kind of approaches is that these take profit of the 3D data during the training stage but then they can use either 3D data (when available) or 2D data during the recognition stage. Belonging to this category, some of 2D statistical approaches like Eigenfaces of Fisherfaces have been extended to fit in this new paradigm leading to the Partial Principal Component Analysis (P2CA) approach. This algorithm intends to cope with big pose variations (±90 ∘) by using 180∘ cylindrical texture maps for training the system but then only images acquired from a single, normal camera are used for the recognition. These training images provide pose information from different views (2.5D data). Nevertheless they can also be extended to a complete 3D multimodal system where depth and texture information is used. This chapter is structured as follows: First, a brief overview of the state-of-the-art in face recognition is introduced. The most relevant methods are grouped by multimedia scenarios and concrete applications. Afterwards, novel 2D-3D mixed face recognition approaches will be introduced.KeywordsFace RecognitionLocal Binary PatternMultimedia ApplicationActive Appearance ModelFace SpaceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Texture mapping is a common operation to increase the realism of three-dimensional meshes at low cost. We propose a new texture optimization algorithm based on the reduction of the physical space allotted to the texture image. Our algorithm optimizes the use of texture space by computing a warping function for the image and new texture coordinates. Neither the mesh geometry nor its connectivity are modified by the optimization. Our method uniformly distributes frequency content of the image in the spatial domain. In other words, the image is stretched in high frequency areas, whereas low frequency regions are shrunk. We also take into account distortions introduced by the mapping onto the model geometry in this process. The resulting image can be resampled at lower rate while preserving its original details. The unwarping is performed by the texture mapping function. Hence, the space-optimized texture is stored as-is in texture memory and is fully supported by current graphics hardware. We present several examples showing that our method significantly decreases texture memory usage without noticeable loss in visual quality.

In data-parallel computing that uses a graphic processing unit (GPU), processing of large data requires that multiple GPUs be used in the computer to increase its execution performance. Increasing processing performance by using multiple computing resources has been enabled by the development of computing frameworks based on a directed acyclic graph (DAG). However, their performance degrades in out-of-core processing, which often occurs in processing of large data on GPUs with limited memory capacity. The GPU data input/output (I/O) for data swapping between host memory and GPU memory during the execution of a user DAG is usually a performance bottleneck. A computing framework called is proposed to overcome this drawback. It uses a novel scheduler that involves two methods to minimize the total amount of GPU data I/O of data swapping. First, it performs locality-aware scheduling. When it schedules a task, it selects one that requires the minimum amount of data swapping and reuses as much of the data residing in GPU memory as possible. Second, it extends the locality-aware scheduling so that GPUs can execute data prefetching. Prefetching data that are swapped out from a GPU enables efficient use of bottleneck GPU I/O resources. To prefetch the input data of future tasks, it is required to determine the schedule of future tasks. Victream's scheduler (hereafter, the Victream scheduler) extends the locality-aware scheduling so that it can schedule future tasks to enable data prefetching that is executed in the way that minimizes the amount of data I/O of data swapping. Evaluation of a Victream prototype showed that the performance of Victream is better than that of conventional frameworks by up to 117%.

High-resolution soil property maps (SPMs) are of high relevance on multiple spatial scales. At field-scale, highly resolved knowledge of soil properties can inform soil management zone delineation for precision agriculture applications like irrigation, fertilisation or compaction risk management. On regional to national scales, high-resolution SPMs can aid in formulating soil policy with accurate baselines and realistic improvement goals. They may assist in soil unit definition, aiding in the balancing act between scientific precision and geographic cohesion, i.e. administrative effectiveness. On a continental, European scale, these SPMs can inform on the adequacy of Soil Monitoring Law implementation and can increase the representativity of continental soil sampling campaigns.Contributing to this effort, we produced 10 m-resolution maps of soil pH, bulk density, coarse fragments, and texture (sand, silt, clay). This represents a large increase in spatial resolution compared to previously published maps, whose cells cover at least 250 x 250 m. We generated our maps by training shallow property-specific artificial neural networks (ANNs) on soil sample data across the EU-27 + the United Kingdom. We used soil data from the Land Use/Coverage Area frame statistical Survey (LUCAS): texture and coarse fragments data from the 2009 campaign, and pH and bulk density data from 2018. Instead of collecting a large number of spatial covariate data layers, we used the AlphaEarth Foundations satellite embeddings, produced by Google DeepMind. The satellite embeddings are the result of a representation learning model trained to efficiently compress Earth observation data. In this way, data from various sources is represented as a 64-dimensional vector per 10 m pixel. We extracted the 2018 embeddings as predictors for all soil properties. After training the models, we produced the full maps on Google Earth Engine (GEE) using a hand-implemented ANN with five neurons in the hidden layer, followed by a dropout layer. The dropout layer gave us the opportunity to additionally provide a prediction uncertainty map per soil property. A single output model was used to produce pH, bulk density and coarse fragments, while a multioutput NN generated the three texture components. Model performances varied, with R² ranging from 0.21 (coarse fragments) to 0.69 (pH). We validated our maps using a variety of field- and regional-scale soil datasets, furthermore by comparison of the value distributions with LUCAS data on European and national scales, and by visually contrasting with published soil property maps. We look forward to testing the temporal stability of our models once LUCAS 2022 data are available. Our soil maps will be necessary and useful for the scientific community across scales, from the field to the continent.

We propose a novel disparity-compensated coding algorithm using MAC (multiple auxiliary component) of MPEG-4 for stereoscopic video. MAC was added to the MPEG-4 visual version 2 in order to describe the transparency of the video object. The major difference between the existing coding method and the proposed coding method is the addition of the residual texture coding. The proposed coding method assigns disparity map and residual texture to 3 components of MAC: one component for disparity map and the remaining 2 components for the luminance and chrominance data of the residual texture, respectively.

Abstract. There are some problems in the visualization of 3D model of buildings at present, such as redundant texture data and large memory occupation of loading texture data, which pose challenges to the smooth loading of 3D building visualization. In this paper, we propose a method which using fractal quadtrees to simplify texture data and compress management. Firstly, this method uses the fractal self-similarity and the feature that the same texture has the same fractal dimension to screen all the textures, when the fractal dimension of multiple textures is within the threshold range. Radon transform will be performed on these textures to calculate their standard deviation and further simplify the texture. Secondly, we use fractal texture compression to create a multi-resolution texture data structure with quadtree structure, so that the building 3D model can achieve dynamic visualization of different scales. Finally, the parallel texture mapping of all faces of the 3D model of the building is implemented according to the texture calling rules. Experimental results demonstrate that the texture is managed by the method proposed in this paper, which shortens the loading time of texture and reduces the memory occupation by about 48.92%. Therefore, the method of texture simplification and compression proposed in this paper has a significant effect in the process of building 3D model visualization.

Non-volatile memory (NVM) is expected to be the second level memory (named remote memory) in two-level memory hierarchy in the future. However, NVM has the limited write endurance, thus it is vital to reduce the number of write operations on NVM. Meanwhile, in two-level memory hierarchy, prefetch is widely used for fetching certain data before it is actually required, to hide the remote memory access latency. In general, large-scale nested loop is the performance bottleneck in one program due to the write operations on NVM caused by the first level memory (named local memory) miss and data reuse. Loop tiling is the key technique for grouping iterations so as to reduce the communication with remote memory used in compiler. In this paper, we propose a new loop tiling approach for minimizing the write operations on NVMs and completely hiding the NVM access latency. Specifically, we introduce a series of theorems to help loop tiling. Then, a legal tile shape and an optimal tile size selection strategy is proposed according to data dependency and local memory capacity. Furthermore, we propose a pipeline scheduling policy to completely hide the remote memory latency. Extensive experiments show that the proposed techniques can reduce write operations on NVMs by 95.1% on average, and NVM latency can be completely hidden.

High-fidelity anatomical models can be produced with three-dimensional (3D) scanning techniques and as such be digitally preserved, archived, and subsequently rendered through various media. Here, a novel methodology-digital body preservation-is presented for combining and matching scan geometry with radiographic imaging. The technique encompasses joining layers of 3D surface scans in an anatomical correct spatial relationship. To do so, a computed tomography (CT) volume is used as template to join and merge different surface scan geometries by means of nonrigid registration into a single environment. In addition, the use and applicability of the generated 3D models in digital learning modalities is presented. Finally, as computational expense is usually the main bottleneck in extended 3D applications, the influence of mesh simplification in combination with texture mapping on the quality of 3D models was investigated. The physical fidelity of the simplified meshes was evaluated in relation to their resolution and with respect to key anatomical features. Large- and medium-scale features were well preserved despite extensive 3D mesh simplification. Subtle fine-scale features, particular in curved areas demonstrated the major limitation to extensive mesh size reduction. Depending on the local topography, workable mesh sizes ranging from 10% to 3% of the original size could be obtained, making them usable in various learning applications and environments.

In this paper, we propose a salient region detection method for textured 3D models. We define the salient region, not only based on geometry properties, such as mean curvature, but also based on texture properties, such as texture color. Feature maps of geometry and texture are combined to form a saliency map for capturing the visually important regions of a textured 3D model. During the feature maps construction, we apply a novel filter window to obtain proper sample vertices and texels from the 3D geometry domain and 2D texture image domain. Given the saliency map, we have developed a model reduction method, which comprises the geometry simplification mechanism and the texture space optimization mechanism. Experimental results show that the salient regions of a model can be well preserved even if the model texture and geometry are simplified heavily.

3D models are often lacking a photorealistic appearance, due to low quality of the acquired texture, or to the complete absence of it. Moreover, especially in case of reality based models, it is often of specific interest to texture with images different from photos, like multispectral/multimodal views (InfraRed, X-rays, UV fluorescence etc), or images taken in different moments in time. In this work, a fully automatic approach for texture mapping is proposed. The method relies on the automatic extraction from the model geometry of appropriate depth maps, in form of images, whose pixels maintain an exact correspondence with vertices of the 3D model. A multiresolution method is here proposed to speed up the automatic texturing phase. Maximization of Mutual Information (MMI) is used as similarity measure as it proved to optimally exploit shared information, discarding unrelated features. 3D texturing is then applied to the portion of the model which is visualized in the texture.

This paper studies on dynamic order strategy for auto spare parts on a target service level. A new three-dimensional classification is proposed integrated order policy of spare parts. It improves the present the weakness of ABC classification and combines with three control variables influenced the order policy, X demand, Y item price and Z variation of demand, to classify all spare parts. Unlike most extant inventory-control approaches, Strategy(R, s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> , S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sub> ) offered is a kind of dynamic order strategy integrated with inventory control parameters with inventory check cycle (R), dynamic reorder point (s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> ) and dynamic order quantity (S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sub> ) and is set the goal with a target service level while minimizing of total inventory cost. It takes into accounts the dynamic requirements in category, time and quantity in accordance with automobile spare parts. Simulation experiments were performed by ARENA 10.0 to represent the performance of strategy (R, s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> , S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sub> ) and the optimized strategy is given. Outcomes simulating show that dynamic strategy (R, s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> , S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sub> ) enables to carry out dynamic inventory management and control by parameters R, s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> , S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sub> for automobile spare parts. Also a goal under a certain target service level while satisfied the minimization of total inventory cost can be come true.

Dynamic Energy Management Strategy of Hybrid Electric Vehicles based on Velocity Prediction