As data sets grow to exascale, automated data analysis and visualization are increasingly important, to intermediate human understanding and to reduce demands on disk storage via in situ analysis. Trends in architectu...
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As data sets grow to exascale, automated data analysis and visualization are increasingly important, to intermediate human understanding and to reduce demands on disk storage via in situ analysis. Trends in architecture of high performance computing systems necessitate analysis algorithms to make effective use of combinations of massively multicore and distributed systems. One of the principal analytic tools is the contour tree, which analyses relationships between contours to identify features of more than local importance. Unfortunately, the predominant algorithms for computing the contour tree are explicitly serial, and founded on serial metaphors, which has limited the scalability of this form of analysis. While there is some work on distributed contour tree computation, and separately on hybrid GPU-CPU computation, there is no efficient algorithm with strong formal guarantees on performance allied with fast practical performance. We report the first shared SMP algorithm for fully parallel contour tree computation, with formal guarantees of O(lg V lgt) parallel steps and O(V lg V) work for data with V samples and t contour tree supernodes, and implementations with more than 30x parallel speed up on both CPU using TBB and GPU using Thrust and up 70x speed up compared to the serial sweep and merge algorithm.
This research aims to investigate how creation and skill are taught in design education, and how the ratio of both factors should be arranged in the teaching plan of a computer graphics course. It also asks how the cr...
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This research aims to investigate how creation and skill are taught in design education, and how the ratio of both factors should be arranged in the teaching plan of a computer graphics course. It also asks how the creative-oriented and skill-based teachings influence students on design works. This research conducted two phases of experimental teachings (creative-oriented teaching and skill-based teaching) to investigate the differences of student learning performance and outcomes during these two phases. The creative-oriented teaching phase focused on design thinking and creation inspiring; the skill-based teaching phase focused on software function introduction and skill practice. Questionnaires of learning performance were collected from students after each teaching phase for result analysis. The expert interviews and group discussion were also conducted to collect data. The quantitative data from expert censorship of design works and learning performance questionnaire was computed using the SPSS for Windows. Additionally, information from expert interviews and group discussion was adopted as the qualitative data in this study. Some suggestions are proposed from the research results, and these suggestions are offered for the design courses that were designed for the Science College students. Due to the college of science students being more familiar with computer techniques and software knowledge than the college of arts and design majors, the design course should be planned to be more concentrated on creative-oriented teaching, such as creation inspiring, design procedure teaching, and practical design knowledge guiding. Also, the design course should contain small parts of skill-based teaching, for example the introduction of basic tool and practice of frequent using tool of design software.
Small object arrangement is very important for creating detailed and realistic 3D indoor scenes. In this article, we present an interactive framework based on active learning to help users create customized arrangemen...
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Small object arrangement is very important for creating detailed and realistic 3D indoor scenes. In this article, we present an interactive framework based on active learning to help users create customized arrangements for small objects according to their preferences. To achieve this with minimal user effort, we first learn the prior knowledge about small object arrangement from a 3D indoor scene dataset through a probability mining method, which forms the initial guidance for arranging small objects. Then, users are able to express their preferences on a few small object categories, which are automatically propagated to all the other categories via a novel active learning approach. In the propagation process, we introduce a novel metric to obtain the propagation weights, which measures the degree of interchangeability between two small object categories, and is calculated based on a spatial embedding model learned from the small object neighborhood information extracted from the 3D indoor scene dataset. Experiments show that our framework is able to help users effectively create customized small object arrangements with little effort.
We consider the problem of approximating given shapes so that the surface normals are restricted to a prescribed discrete set. Such shape approximations are commonly required in the context of manufacturing shapes. We...
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We consider the problem of approximating given shapes so that the surface normals are restricted to a prescribed discrete set. Such shape approximations are commonly required in the context of manufacturing shapes. We provide an algorithm that first computes maximal interior polytopes and, then, selects a subset of offsets from the interior polytopes that cover the shape. This provides prescribed Hausdorff error approximations that use only a small number of primitives.
We present a data-driven algorithm for generating gaits of virtual characters with varying dominance traits. Our formulation utilizes a user study to establish a data-driven dominance mapping between gaits and dominan...
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We present a data-driven algorithm for generating gaits of virtual characters with varying dominance traits. Our formulation utilizes a user study to establish a data-driven dominance mapping between gaits and dominance labels. We use our dominance mapping to generate walking gaits for virtual characters that exhibit a variety of dominance traits while interacting with the user. Furthermore, we extract gait features based on known criteria in visual perception and psychology literature that can be used to identify the dominance levels of any walking gait. We validate our mapping and the perceived dominance traits by a second user study in an immersive virtual environment. Our gait dominance classification algorithm can classify the dominance traits of gaits with similar to 73 percent accuracy. We also present an application of our approach that simulates interpersonal relationships between virtual characters. To the best of our knowledge, ours is the first practical approach to classifying gait dominance and generate dominance traits in virtual characters.
With the development of AR/VR technologies, a reliable and straightforward way to digitize a threedimensional human body is in high demand. Most existing methods use complex equipment and sophisticated algorithms, but...
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With the development of AR/VR technologies, a reliable and straightforward way to digitize a threedimensional human body is in high demand. Most existing methods use complex equipment and sophisticated algorithms, but this is impractical for everyday users. In this paper, we propose a pipeline that reconstructs a 3D human shape avatar from a single image. Our approach simultaneously reconstructs the three-dimensional human geometry and whole body texture map with only a single RGB image as input. We first segment the human body parts from the image and then obtain an initial body geometry by fitting the segment to a parametric model. Next, we warp the initial geometry to the final shape by utilizing a silhouette-based dense correspondence. Finally, to infer invisible back texture from a frontal image, we propose a network called InferGAN. Based on human semantic information, we also propose a method to handle partial occlusion by reconstructing the occluded body parts separately. Comprehensive experiments demonstrate that our solution is robust and effective on both public and our own datasets. Our human avatars can be easily rigged and animated using MoCap data. We have developed a mobile application that demonstrates this capability for AR applications. (c) 2021 Elsevier Ltd. All rights reserved.
Increasing human activities have caused serious disturbance to global forest resources, so how to accurately identify individual trees has become an important task of forest resources investigation. In order to get th...
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Increasing human activities have caused serious disturbance to global forest resources, so how to accurately identify individual trees has become an important task of forest resources investigation. In order to get the accurate number of individual trees, this paper took coniferous forest and mixed coniferous and broad-leaved forest as experimental samples, as well as Digital Orthophoto Map and airborne LiDAR Point Cloud as research data. We propose a deep Learning individual tree segmentation method based on RetinaNet model and PCS algorithm by doing comparative analysis (classical Watershed Algorithm and Layer Stacking Algorithm) at the plots (with high, medium, and low densities). The experimental results show that the method proposed in this paper can solve the problem of individual tree segmentation in high density forest and improve its degree of automation. Compared with Watershed algorithm and Layer stacking algorithm, F-Measure is improved by 6%-29% and 7%-20%, respectively. In other words, the results of individual tree segmentation presented in this paper can not only improve the precision of individual tree segmentation, but also maintain a high detection rate, which can meet the accuracy and high efficiency of individual tree extraction, so as to meet the needs for modern forestry investigation.
Motion Blur is an important effect of photo-realistic rendering. Distribution ray tracing can simulate mo-tion blur very well by integrating light, both over the spatial and the temporal domain. However, increas-ing t...
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Motion Blur is an important effect of photo-realistic rendering. Distribution ray tracing can simulate mo-tion blur very well by integrating light, both over the spatial and the temporal domain. However, increas-ing the problem by the temporal dimension entails many challenges, particularly in cinematic multi-bounce path tracing of complex scenes, where heavy-weight geometry with complex lighting and even offscreen elements contribute to the final image. In this paper, we propose the Motion DAG (Directed Acyclic Graph), a novel data structure that filters an entire animation sequence of an object, both in the spatial and temporal domain. These Motion DAGs interleave a temporal interval binary tree for filtering time consecutive data and a sparse voxel octree (SVO), which simplifies spatially nearby data. Motion DAGs are generated in a pre-process and can be easily integrated in a conventional physically based path tracer. Our technique is designed to target motion blur of small objects, where coarse representations are sufficient. Specifically, in this scenario our results show that it is possible to significantly reduce both, memory consumption and render time. (c) 2021 Elsevier Ltd. All rights reserved.
Extraction of multiscale features using scale-space is one of the fundamental approaches to analyze scalar fields. However, similar techniques for vector fields are much less common, even though it is well known that,...
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Extraction of multiscale features using scale-space is one of the fundamental approaches to analyze scalar fields. However, similar techniques for vector fields are much less common, even though it is well known that, for example, turbulent flows contain cascades of nested vortices at different scales. The challenge is that the ideas related to scale-space are based upon iteratively smoothing the data to extract features at progressively larger scale, making it difficult to extract overlapping features. Instead, we consider spatial regions of influence in vector fields as scale, and introduce a new approach for the multiscale analysis of vector fields. Rather than smoothing the flow, we use the natural Helmholtz-Hodge decomposition to split it into small-scale and large-scale components using progressively larger neighborhoods. Our approach creates a natural separation of features by extracting local flow behavior, for example, a small vortex, from large-scale effects, for example, a background flow. We demonstrate our technique on large-scale, turbulent flows, and show multiscale features that cannot be extracted using state-of-the-art techniques.
In this work, we present a novel dataset, SynthOutdoor , comprising 39,086 high-resolution images, aimed at addressing the data scarcity in the field of 3D light direction estimation under the assumption of distant li...
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In this work, we present a novel dataset, SynthOutdoor , comprising 39,086 high-resolution images, aimed at addressing the data scarcity in the field of 3D light direction estimation under the assumption of distant lighting. SynthOutdoor was generated using our software (which is also publicly available), that in turn is based on the Unity3D engine. Our dataset provides a set of images rendered from a given input scene, with the camera moving across a predefined path within the scene. This dataset captures a wide variety of lighting conditions through the implementation of a solar cycle. The dataset's ground truth is composed of the following elements: the 3D light direction and color intensity of the sun;the color intensity of the ambient light;the instance segmentation masks of each object and the surface normals map, in which each pixel is assigned with the 3D surface normal in that point (encoded as 3 color channels). By providing not only the light direction and intensity, but also the geometric and semantic information of the rendered images, our dataset can be used not only for light estimation, but also for more general tasks such as 3D geometry and shading estimation from 2D images. (c) 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC license ( http://***/licenses/by-nc/4.0/ )
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