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"content": "Streamsurfaces are of fundamental importance to visualization of flows. Among other features, they offer strong capabilities in revealing flow behavior (e.g., in the vicinity of vortices), and are an essential tool for the computation of 2D separatrices in vector field topology. Computing streamsurfaces is, however, typically expensive due to the difficult triangulation involved, in particular when triangle sizes are kept in the order of the size of a pixel. We investigate image-based approaches for rendering streamsurfaces without triangulation, and propose a new technique that renders them by dense streamlines. Although our technique does not perform triangulation, it does not depend on user parametrization to avoid noticeable gaps. Our GPU-based implementation shows that our technique provides interactive frame rates and low memory usage in practical applications. We also show that previous texture-based flow visualization approaches can be integrated with our method, for example, for the visualization of flow direction with line integral convolution.",
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"abstract": "This paper details a case study of trade-off design in software synthesis. Domain-oriented software synthesis technology enables software designers to encode their own specific knowledge of software design into transformation rules. Thus, generated software is optimal and actually usable for the domain. However, optimal implementation cannot be decided in advance if performance specifications, e.g., memory size limits and execution time limits, are unclear. That is, it is difficult to develop transformation rules which generate optimal software for their domain or situation if performance requirements are unclear beforehand. This paper proposes a method of trade-off design in software synthesis, and applies this method to develop a file access program generator called POT-DB. The proposed method includes procedures for (1) extracting trade-off parameters as input specifications, (2) designing transformation rules for trade-off parameters to generate programs, and (3) designing performance measurement rules to allow designers to notice the effects of trade-off parameters. Based on the results of applying POT-DB in developing a sales analysis and ordering system, all performance requirements have been satisfied, and application productivity has been improved 1.9 times. Moreover, it is shown that total productivity including the development cost for the POT-DB itself can be improved if POT-DB is applied to at least four application systems. The developed sales analysis and ordering system has been in daily operation with over 10,000 portable terminals at more than one hundred branch stores.",
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"abstract": "Literature suggests that note-taking activity helps students in their learning process and successfully increases performance. Previous studies also have suggested that collaborative learning facilitates students to learn from different views of interpreting information. Although many studies have revealed positive correlations between collaborative learning and student performance, few studies have been conducted to investigate peer-review activity, students' performance, and self-regulated learning skills while engaged in shared note-taking using electronic enhanced guided notes. The main research question of the current study was to investigate how students' review frequency of peers' enhanced guided notes and learning performance reflected on their self-regulated learning skills. With regards to this question, we specifically explored differences among students according to their peer-review activity and performance and how the differences reflected on their self-regulated learning skills. Our findings revealed four groups of students based on those differentiation factors. Data analysis showed that while sixty percent of participants were willing to review their peers' enhanced guided notes regularly, sixty-eight percent of participants performed very well on the exams. Results also suggest that willingness to review peers' guided notes positively correlated with planning and cognitive strategies. Implications of the use of shared note-taking in an engineering classroom will be discussed.",
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"content": "Literature suggests that note-taking activity helps students in their learning process and successfully increases performance. Previous studies also have suggested that collaborative learning facilitates students to learn from different views of interpreting information. Although many studies have revealed positive correlations between collaborative learning and student performance, few studies have been conducted to investigate peer-review activity, students' performance, and self-regulated learning skills while engaged in shared note-taking using electronic enhanced guided notes. The main research question of the current study was to investigate how students' review frequency of peers' enhanced guided notes and learning performance reflected on their self-regulated learning skills. With regards to this question, we specifically explored differences among students according to their peer-review activity and performance and how the differences reflected on their self-regulated learning skills. Our findings revealed four groups of students based on those differentiation factors. Data analysis showed that while sixty percent of participants were willing to review their peers' enhanced guided notes regularly, sixty-eight percent of participants performed very well on the exams. Results also suggest that willingness to review peers' guided notes positively correlated with planning and cognitive strategies. Implications of the use of shared note-taking in an engineering classroom will be discussed.",
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"abstract": "In this paper I propose new feature vectors for automatic speech recognition. They are based on Mel-cepstrum vectors augmented by derivatives. In the literature, many systems using just two derivatives—delta and delta delta—are described. But none explores the use of higher order derivatives. This paper presents alphabet recognition results on the Isolet database, using feature vectors containing up to the fifth-order derivatives. For this paper I did not use the HTK toolkit proposed by Cambridge University. I developed my own HMM system. I show that with vectors incorporating all the derivatives up to the fifth one, 97.54% mean recognition accuracy was achieved, result which is comparable to the best published one on this database (97.6%), if the recognition accuracy confidence interval concerning this task (approximately 0.3%) is taken into account. It is important to note that this result was obtained without segmenting the speech files by an endpoint detection algorithm. This is an unfavourable experimental condition compared to previous published research works. As a consequence, my system is one of the most powerful systems ever implemented for alphabet recognition.",
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"title": "Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration",
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"abstract": "In this paper, by modeling the point cloud registration task as a Markov decision process, we propose an end-to-end deep model embedded with the cross-entropy method (CEM) for unsupervised 3D registration. Our model consists of a sampling network module and a differentiable CEM module. In our sampling network module, given a pair of point clouds, the sampling network learns a prior sampling distribution over the transformation space. The learned sampling distribution can be used as a \"good\" initialization of the differentiable CEM module. In our differentiable CEM module, we first propose a maximum consensus criterion based alignment metric as the reward function for the point cloud registration task. Based on the reward function, for each state, we then construct a fused score function to evaluate the sampled transformations, where we weight the current and future rewards of the transformations. Particularly, the future rewards of the sampled transforms are obtained by performing the iterative closest point (ICP) algorithm on the transformed state. By selecting the top-k transformations with the highest scores, we iteratively update the sampling distribution. Furthermore, in order to make the CEM differentiable, we use the sparse-max function to replace the hard top-k selection. Finally, we formulate a Geman-McClure estimator based loss to train our end-to-end registration model. Extensive experimental results demonstrate the good registration performance of our method on benchmark datasets. Code is available at https://github.com/Jiang-HB/CEMNet.",
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"content": "In this paper, by modeling the point cloud registration task as a Markov decision process, we propose an end-to-end deep model embedded with the cross-entropy method (CEM) for unsupervised 3D registration. Our model consists of a sampling network module and a differentiable CEM module. In our sampling network module, given a pair of point clouds, the sampling network learns a prior sampling distribution over the transformation space. The learned sampling distribution can be used as a \"good\" initialization of the differentiable CEM module. In our differentiable CEM module, we first propose a maximum consensus criterion based alignment metric as the reward function for the point cloud registration task. Based on the reward function, for each state, we then construct a fused score function to evaluate the sampled transformations, where we weight the current and future rewards of the transformations. Particularly, the future rewards of the sampled transforms are obtained by performing the iterative closest point (ICP) algorithm on the transformed state. By selecting the top-k transformations with the highest scores, we iteratively update the sampling distribution. Furthermore, in order to make the CEM differentiable, we use the sparse-max function to replace the hard top-k selection. Finally, we formulate a Geman-McClure estimator based loss to train our end-to-end registration model. Extensive experimental results demonstrate the good registration performance of our method on benchmark datasets. Code is available at https://github.com/Jiang-HB/CEMNet.",
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"affiliation": "Nanjing University of Science and Technology,PCA Lab,China",
"fullName": "Haobo Jiang",
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"fullName": "Yaqi Shen",
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"affiliation": "Nanjing University of Science and Technology,PCA Lab,China",
"fullName": "Jin Xie",
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"affiliation": "Nanjing University of Science and Technology,PCA Lab,China",
"fullName": "Jun Li",
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{
"affiliation": "Nanjing University of Science and Technology,PCA Lab,China",
"fullName": "Jianjun Qian",
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{
"affiliation": "Nanjing University of Science and Technology,PCA Lab,China",
"fullName": "Jian Yang",
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"abstract": "This paper presents a new task, point cloud object co-segmentation, aiming to segment the common 3D objects in a set of point clouds. We formulate this task as an object point sampling problem, and develop two techniques, the mutual attention module and co-contrastive learning, to enable it. The proposed method employs two point samplers based on deep neural networks, the object sampler and the background sampler. The former targets at sampling points of common objects while the latter focuses on the rest. The mutual attention module explores point-wise correlation across point clouds. It is embedded in both samplers and can identify points with strong cross-cloud correlation from the rest. After extracting features for points selected by the two samplers, we optimize the networks by developing the co-contrastive loss, which minimizes feature discrepancy of the estimated object points while maximizing feature separation between the estimated object and back-ground points. Our method works on point clouds of an arbitrary object class. It is end-to-end trainable and does not need point-level annotations. It is evaluated on the ScanObjectNN and S3DIS datasets and achieves promising results. The source code will be available at https://github.com/jimmy15923/unsup_point_coseg.",
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"normalizedAbstract": "This paper presents a new task, point cloud object co-segmentation, aiming to segment the common 3D objects in a set of point clouds. We formulate this task as an object point sampling problem, and develop two techniques, the mutual attention module and co-contrastive learning, to enable it. The proposed method employs two point samplers based on deep neural networks, the object sampler and the background sampler. The former targets at sampling points of common objects while the latter focuses on the rest. The mutual attention module explores point-wise correlation across point clouds. It is embedded in both samplers and can identify points with strong cross-cloud correlation from the rest. After extracting features for points selected by the two samplers, we optimize the networks by developing the co-contrastive loss, which minimizes feature discrepancy of the estimated object points while maximizing feature separation between the estimated object and back-ground points. Our method works on point clouds of an arbitrary object class. It is end-to-end trainable and does not need point-level annotations. It is evaluated on the ScanObjectNN and S3DIS datasets and achieves promising results. The source code will be available at https://github.com/jimmy15923/unsup_point_coseg.",
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"abstract": "With the fast development of the industrialization and urbanization process in the world, the various issues of air pollution are appearing, and most of all are health-related issues. So the air pollution monitoring should be focused on by the human. The paper reviews the development of the technology of air pollution monitoring, point out the shortages of the current monitoring technology, and bring forward the advantage of the technology of the ZigBee based wireless sensor network in air pollution monitoring, discusses the GIS technology, focusing on the requirements of air pollution monitoring system. Finally, based on the above mentioned, the application schema of the ZigBee based WSN and GIS is designed and discussed in detail.",
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"content": "With the fast development of the industrialization and urbanization process in the world, the various issues of air pollution are appearing, and most of all are health-related issues. So the air pollution monitoring should be focused on by the human. The paper reviews the development of the technology of air pollution monitoring, point out the shortages of the current monitoring technology, and bring forward the advantage of the technology of the ZigBee based wireless sensor network in air pollution monitoring, discusses the GIS technology, focusing on the requirements of air pollution monitoring system. Finally, based on the above mentioned, the application schema of the ZigBee based WSN and GIS is designed and discussed in detail.",
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"abstract": "In connection with the uncertainty problem of pollutants diffusing in the atmosphere of Wuan city, we monitor and put forward a new conception about air ground pollution belt, and get two-dimensional finite difference equation of air pollution. Meanwhile dispersion coefficient and wind speed are considered as grey parameters. One grey numerical quality model of air pollution is built according to grey theory and applies to the air pollution diffusing system of Wuan city, and simulates SO2 and PM10 during heating times and unheating times. The result shows the gray numerical model is suitable for application. Meantime, according to the simulation result, put forward the control measures of air pollution in Wuan city.",
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"abstract": "We propose three modeling methods using a mobile sensor network to generate high spatio-temporal resolution air pollution maps for urban environments. In our deployment in Lausanne (Switzerland), dedicated sensing nodes are anchored to the public buses and measure multiple air quality parameters including the Lung Deposited Surface Area (LDSA), a state of the art metric for quantifying human exposure to ultra fine particles. In this paper, our focus is on generating LDSA maps. In particular, since the sensor network coverage is spatially and temporally dynamic, we leverage models to estimate the values for the locations and times where the data are not available. We first discretize the area topologically based on the street segments in the city and we then propose the following three prediction models: i) a log-linear regression model based on nine meteorological (e.g., Temperature and precipitations) and gaseous (e.g., NO 2 and CO) explanatory variables measured at two static stations in the city, ii) a novel network-based log-linear regression model that takes into account the LDSA values of the most correlated streets and also the nine explanatory variables mentioned above, iii) a novel Probabilistic Graphical Model (PGM) in which each street segment is considered as one node of the graph, and inference on conditional joint probability distributions of the nodes results in estimating the values in the nodes of interest. More than 44 millions of geo- and time-stamped LDSA measurements (i.e., More than 14 months of real data) are used in this paper to evaluate the proposed modeling approaches in various time resolutions (hourly, daily, weekly and monthly). The results show that the three approaches bring significant improvements in R2, RMSE and FAC metrics compared to a baseline K-Nearest Neighbor method.",
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"abstract": "Despite the fact that face detection has been studied intensively over the past several decades, the problem is still not completely solved. Challenging conditions, such as extreme pose, lighting, and occlusion, have historically hampered traditional, model-based methods. In contrast, exemplar-based face detection has been shown to be effective, even under these challenging conditions, primarily because a large exemplar database is leveraged to cover all possible visual variations. However, relying heavily on a large exemplar database to deal with the face appearance variations makes the detector impractical due to the high space and time complexity. We construct an efficient boosted exemplar-based face detector which overcomes the defect of the previous work by being faster, more memory efficient, and more accurate. In our method, exemplars as weak detectors are discriminatively trained and selectively assembled in the boosting framework which largely reduces the number of required exemplars. Notably, we propose to include non-face images as negative exemplars to actively suppress false detections to further improve the detection accuracy. We verify our approach over two public face detection benchmarks and one personal photo album, and achieve significant improvement over the state-of-the-art algorithms in terms of both accuracy and efficiency.",
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"abstract": "In order to solve the defects of traditional point cloud registration iterative nearest point (ICP) algorithm in the case of random initial rotation and translation matrix, it is easy to fall into local optimal, time-consuming iteration, and lacks self-adjustment ability, and meet the real-time requirements of using lidar to collect vehicle contour information. A three-stage point cloud registration mode optimized by annealing algorithm is proposed. Through the three-stage point cloud registration mode, the first stage is point-to-point registration, and the simulated annealing algorithm is used to select the matching pairs of feature points. The second stage registration is the registration of features. The first stage registration is used as input to construct feature point clouds for registration. The third part is point cloud to point cloud registration, and the ICP algorithm of KD-tree search optimization is used for the overall registration. Experimental results show that. This algorithm can obtain better vehicle contour point cloud, and by comparing classical ICP, principal component analysis (PCA)-ICP, FPFH-ICP and other algorithms, the algorithm proposed in this paper takes less time to register different environments and objects. The complete car contour can provide the basis for the control of the subsequent robot and has reference significance for the study of laser point cloud processing.",
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"content": "In order to solve the defects of traditional point cloud registration iterative nearest point (ICP) algorithm in the case of random initial rotation and translation matrix, it is easy to fall into local optimal, time-consuming iteration, and lacks self-adjustment ability, and meet the real-time requirements of using lidar to collect vehicle contour information. A three-stage point cloud registration mode optimized by annealing algorithm is proposed. Through the three-stage point cloud registration mode, the first stage is point-to-point registration, and the simulated annealing algorithm is used to select the matching pairs of feature points. The second stage registration is the registration of features. The first stage registration is used as input to construct feature point clouds for registration. The third part is point cloud to point cloud registration, and the ICP algorithm of KD-tree search optimization is used for the overall registration. Experimental results show that. This algorithm can obtain better vehicle contour point cloud, and by comparing classical ICP, principal component analysis (PCA)-ICP, FPFH-ICP and other algorithms, the algorithm proposed in this paper takes less time to register different environments and objects. The complete car contour can provide the basis for the control of the subsequent robot and has reference significance for the study of laser point cloud processing.",
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"abstract": "This paper addresses the use of Virtual Reality (VR) in Science, Technology, Engineering, and Math (STEM) education. There are limited studies investigating the proper design and effectiveness of VR in STEM education, and current VR frameworks and applications lack explicit links to the established learning theories and assessment mechanisms to evaluate learning outcomes. We present ScienceVR, an educational virtual reality design framework, illustrated through a science laboratory prototype, to bridge some of the gaps identified in the design and development of a VR environment for learning. We established design guidelines and implemented an in-app data collection system to measure users’ learning, performance, and task completion rate. Our evaluation using ANOVA and other non-parametric methods with 36 participants in three groups: immersive VR (IVR), desktop VR(DVR), and 2D indicated improved usability and learning outcomes for the IVR group. Task completion rate in the IVR group was higher (68% compared to DVR with 50%). For memorability, the IVR condition performed better than DVR while for learnability, IVR&DVR performed significantly better than 2D. IVR group has performed better and faster with more accuracy compared to the DVR group in completing the tasks.",
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"abstract": "Body-centric locomotion allows users to navigate virtual environments with body parts (e.g. head tilt, arm swing or torso lean). Transfer functions are an important determinant of the locus of such a locomotion method. However, there is little known about the effects of transfer functions on virtual locomotion with different body parts. In this work, we selected four typical transfer functions (linear function: L, power function: P, a piecewise function with constant and linear functions: CL, and a piecewise function with constant and power functions: CP) and four body parts (head, arm, torso, and knee) from existing works, and conducted an experiment to evaluate their effects on virtual locomotion under three distances (5, 10, and 15 m) in Virtual Reality (VR). Results show that (1) CP function generally led to the longest task time with a low rate of failed trials, while CL function had the shortest task time with a high rate of failed trials; (2) body parts significantly affected the rate of failed trials, but not task time and final position offset. Head and torso resulted in the lowest and highest rate of failed trials respectively; (3) body parts did not differ in User Experience Questionnaire-Short (UEQ-S), UEQ-S Pragmatic and UEQ-S Hedonic. L was rated as the highest score for UEQ-S, UEQ-S Pragmatic and UEQ-S Hedonic, but CP had the lowest score. According to the results, we provide implications of designing body-centric locomotion with different transfer functions in VR.",
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"abstract": "Face recognition systems are susceptible to presentation attacks such as printed photo attacks, replay attacks, and 3D mask attacks. These attacks, primarily studied in visible spectrum, aim to obfuscate or impersonate a person's identity. This paper presents a unique multispectral video face database for face presentation attack using latex and paper masks. The proposed Multispectral Latex Mask based Video Face Presentation Attack (MLFP) database contains 1350 videos in visible, near infrared, and thermal spectrums. Since the database consists of videos of subjects without any mask as well as wearing ten different masks, the effect of identity concealment is analyzed in each spectrum using face recognition algorithms. We also present the performance of existing presentation attack detection algorithms on the proposed MLFP database. It is observed that the thermal imaging spectrum is most effective in detecting face presentation attacks.",
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"title": "A Binocular Model to Evaluate User Experience in Ophthalmic and AR Prescription Lens Designs",
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"abstract": "Supporting refractive correction in head-mounted AR systems is central for providing accessibility across a diverse population. Importantly, matching the visual experience between a user's traditional ophthalmic lenses and their AR devices is critical for a seamless and comfortable user experience. In terms of geometric distortion, this can be challenging with the addition of optical and non-optical elements that accompany the AR experience. We developed an analytical model that quantifies the binocular aspects of optically induced geometric distortions with an aim to develop perceptually based metrics that can predict the user's experience with different spectacle lens designs. We anchored this model against empirical data collected from a small study where we systematically varied the front surface curvature of a user's habitual refractive correction. Importantly, the models we derived explained a significant amount of variance (r2 = 0.46 to 0.92) in users' reported visual comfort. These results support the value of quantifying the binocular aspects of the visual experience in see-through optical systems and lay a foundation for a user-centric, quantitative system that can be used to evaluate optical lens designs in both the ophthalmic and near-eye display industries.",
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"content": "Supporting refractive correction in head-mounted AR systems is central for providing accessibility across a diverse population. Importantly, matching the visual experience between a user's traditional ophthalmic lenses and their AR devices is critical for a seamless and comfortable user experience. In terms of geometric distortion, this can be challenging with the addition of optical and non-optical elements that accompany the AR experience. We developed an analytical model that quantifies the binocular aspects of optically induced geometric distortions with an aim to develop perceptually based metrics that can predict the user's experience with different spectacle lens designs. We anchored this model against empirical data collected from a small study where we systematically varied the front surface curvature of a user's habitual refractive correction. Importantly, the models we derived explained a significant amount of variance (r2 = 0.46 to 0.92) in users' reported visual comfort. These results support the value of quantifying the binocular aspects of the visual experience in see-through optical systems and lay a foundation for a user-centric, quantitative system that can be used to evaluate optical lens designs in both the ophthalmic and near-eye display industries.",
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"doi": "10.1109/VR.2019.8798226",
"title": "A New 360 Camera Design for Multi Format VR Experiences",
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"abstract": "We present a new 360 camera design for creating 360 videos for immersive VR experiences. We place eight fish-eye lenses on a circle. Four interlaced fish-eye lenses are slightly re-oriented up in order to cover the scene above. To the best of our knowledge, our camera has the smallest diameter of any existing stereo multi-lens rig on the market. Our camera can be used to create 2D, 3D and 6DoF multi-format 360 videos. Due to its compact design, the minimum safe distance of our new camera is very short (approximately 30cm). This allows users to create special intimate immersive experiences. We also propose to characterize the camera design using the fractal ratio of the distance of adjacent view points and interpupillary distance. While most early camera designs have fractal ratio or =1, our camera has the fractal ratio . Moreover, with adjustable rendering interpupillary distance, our camera can be used to flexibly control the interpupillary distance for creating 3D 360 videos. Our camera design has high fault tolerance and it can continue operating properly even in the event of the failure of some individual lenses.",
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"abstract": "Discrete point cloud objects lack sufficient shape descriptors of 3D geometries. In this paper, we present a novel method for aggregating hypothetical curves in point clouds. Sequences of connected points (curves) are initially grouped by taking guided walks in the point clouds, and then subsequently aggregated back to augment their pointwise features. We provide an effective implementation of the proposed aggregation strategy including a novel curve grouping operator followed by a curve aggregation operator. Our method was benchmarked on several point cloud analysis tasks where we achieved the state-of-the-art classification accuracy of 94.2% on the ModelNet40 classification task, instance IoU of 86.8% on the ShapeNetPart segmentation task and cosine error of 0.11 on the ModelNet40 normal estimation task. Our project page with source code is available at: https://curvenet.github.io/.",
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"affiliation": "University of Sydney",
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