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Feasibility study of a serious game based on Kinect system for functional rehabilitation of the lower limbs

European Research in Telemedicine 2016年第3期5卷 97-104页

作者： Tannous, H. Istrate, d. Ho Ba Tho, M.C. dao, T.T. Sorbonne Universités Université de Technologie de Compiègne CNRS UMR 7338 Biomécanique et Bioingénierie Centre de Recherche Royallieu CS 60319 Compiègne 60203 France

Introduction Conventional functional rehabilitation costs time, money and effort for the patients and for the medical staff. Serious games have been used as a new approach to improve the performance as well as to possibly reduce medical cost in the future for cognitive rehabilitation and body balance control. The objective of this present work was to perform a feasibility study on the use of a new real-time serious game system for improving the musculoskeletal rehabilitation of the lower limbs. Materials and methods A basic functional rehabilitation exercise database was established with different levels of difficulties. A 3d virtual avatar was created and scaled to represent each subject-specific body. A portable and affordable Kinect sensor was used to capture real-time kinematics during each exercise. A specific data coupling process was developed. An evaluation campaign was established to assess the developed system. Results The squats exercise was the hardest challenge. Moreover, the performance of each functional rehabilitation exercise depended on the physiological profile of each participant. Our game system was clear and attractive for all functional rehabilitation exercises. All testing subjects felt motivated and secure when playing the rehabilitation game. discussion The comparison with other systems showed that our system was the first one focusing on the functional rehabilitation exercises of the lower limbs. Conclusions Our system showed useful functionalities for a large range of applications (rehabilitation at home, sports training). Looking forward, new in-situation exercises will be investigated for specific musculoskeletal disorders. © 2016 Elsevier Masson SAS

关键词： 3d computer vision Functional rehabilitation Kinect sensor Musculoskeletal system Real-time monitoring Rehabilitation at home Serious game

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CVTHead: One-shot Controllable Head Avatar with Vertex-feature Transformer

CVTHead: One-shot Controllable Head Avatar with Vertex-featu...

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IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Ma, Haoyu Zhang, Tong Sun, Shanlin Yan, Xiangyi Han, Kun Xie, Xiaohui Univ Calif Irvine Irvine CA 92717 USA

ISBN: (纸本)9798350318920;9798350318937

Reconstructing personalized animatable head avatars has significant implications in the fields of AR/VR. Existing methods for achieving explicit face control of 3d Morphable Models (3dMM) typically rely on multi-view images or videos of a single subject, making the reconstruction process complex. Additionally, the traditional rendering pipeline is time-consuming, limiting real-time animation possibilities. In this paper, we introduce CVTHead, a novel approach that generates controllable neural head avatars from a single reference image using point-based neural rendering. CVTHead considers the sparse vertices of mesh as the point set and employs the proposed Vertex-feature Transformer to learn local feature descriptors for each vertex. This enables the modeling of long-range dependencies among all the vertices. Experimental results on the VoxCeleb dataset demonstrate that CVTHead achieves comparable performance to state-of-the-art graphics-based methods. Moreover, it enables efficient rendering of novel human heads with various expressions, head poses, and camera views. These attributes can be explicitly controlled using the coefficients of 3dMMs, facilitating versatile and realistic animation in real-time scenarios. Codes and pre-trained model can be found at https://***/HowieMa/CVTHead.

关键词： 3d computer vision Algorithms Algorithms Biometrics body pose face gesture

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Indoor Visual Localization using Point and Line Correspondences in dense colored point cloud

Indoor Visual Localization using Point and Line Corresponden...

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IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Matsumoto, Yuya Nakano, Gaku Ogura, Kazumine NEC Corp Ltd Tokyo Japan

ISBN: (纸本)9798350318920;9798350318937

We propose a novel pipeline called Loc-PL that uses both points and lines for indoor visual localization in dense colored point cloud. Loc-PL utilizes the spatially complementary relationship between points and lines to address challenging indoor issues. There are two successive camera pose estimation modules. The first improves robustness against repetitive patterns by considering the geometric consistency of points and lines. The second utilizes points and lines to refine poses by Perspective-m-Point-n-Line (PmPnL) and circumvents unstable localization due to locally concentrated matches caused by less-textured environments. The modules use different schemes to obtain line correspondences;the first finds line matches using RANSAC, which is effective for image pairs with large viewpoint gaps, and the second utilizes rendered images from dense point cloud to get them by feature line matching. In addition, we develop a simple but effective module for evaluating the correctness of camera poses using matched point distances across two images. The experimental results on a large dataset, InLoc, show that Loc-PL achieves the state-of-the-art in four out of six scores.

关键词： 3d computer vision Algorithms Applications Robotics

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Learning Robust Features and Latent Representations for Single View 3d Pose Estimation of Humans and Objects

Learning Robust Features and Latent Representations for Sing...

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作者： Bugra TEKIN ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE

学位级别：博士

Estimating the 3d poses of rigid and articulated bodies is one of the fundamental problems of computer vision. It has a broad range of applications including augmented reality, surveillance, animation and human-computer interaction. despite the ever-growing demand driven by the applications, predicting 3d pose from a 2d image is a challenging and ill-posed problem due to the loss of depth information during projection from 3d to 2d. Although there have been years of research on 3d pose estimation problem, it still remains unsolved. In this thesis, we propose a variety of ways to tackle the 3d pose estimation problem both for articulated human bodies and rigid object bodies by learning robust features and latent representations. First, we present a novel video-based approach that exploits spatiotemporal features for 3d human pose estimation in a discriminative regression scheme. While early approaches typically account for the motion information by temporally regularizing noisy pose estimates in individual frames, we demonstrate that taking into account motion information very early in the modeling process with spatiotemporal features yields significant performance improvements. We further propose a CNN-based motion compensation approach that stabilizes and centralizes the human body in the bounding boxes of consecutive frames to increase the reliability of spatiotemporal features. This then allows us to effectively overcome ambiguities and improve pose estimation accuracy. Second, we develop a novel deep Learning framework for structured prediction of 3d human pose. Our approach relies on an auto-encoder to learn a high-dimensional latent pose representa- tion that accounts for joint dependencies. We combine traditional CNNs for supervised learning with auto-encoders for structured learning and demonstrate that our approach outperforms the existing ones both in terms of structure preservation and prediction accuracy. Third, we propose a 3d human pose estimation appr

关键词： 3d human pose estimation 3d object pose estimation 6d pose estimation 3d computer vision motion compensation deep learning structured prediction

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Effects of Markers in Training datasets on the Accuracy of 6d Pose Estimation

Effects of Markers in Training Datasets on the Accuracy of 6...

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IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Rosskamp, Janis Weller, Rene Zachmann, Gabriel Univ Bremen Comp Graph & Virtual Real Bremen Germany

ISBN: (纸本)9798350318920;9798350318937

Collecting training data for pose estimation methods on images is a time-consuming task and usually involves some kind of manual labeling of the 6d pose of objects. This time could be reduced considerably by using marker-based tracking that would allow for automatic labeling of training images. However, images containing markers may reduce the accuracy of pose estimation due to a bias introduced by the markers. In this paper, we analyze the influence of markers in training images on pose estimation accuracy. We investigate the accuracy of estimated poses for three different cases: i) training on images with markers, ii) removing markers by inpainting, and iii) augmenting the dataset with randomly generated markers to reduce spatial learning of marker features. Our results demonstrate that utilizing marker-based techniques is an effective strategy for collecting large amounts of ground truth data for pose prediction. Moreover, our findings suggest that the usage of inpainting techniques do not reduce prediction accuracy. Additionally, we investigate the effect of inaccuracies of labeling in training data on prediction accuracy. We show that the precise ground truth data obtained through marker tracking proves to be superior compared to markerless datasets if labeling errors of 6d ground truth exist. Our data generation tools are available online: https://***/JHRosskamp/6dPosedataGenTools

关键词： 3d computer vision Algorithms Algorithms datasets and evaluations

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PointCT: Point Central Transformer Network for Weakly-supervised Point Cloud Semantic Segmentation

PointCT: Point Central Transformer Network for Weakly-superv...

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IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Tran, Anh-Thuan Le, Hoanh-Su Lee, Suk-Hwan Kwon, Ki-Ryong Pukyong Natl Univ Dept Artificial Intelligence Convergence Busan South Korea Dong A Univ Dept Comp Engn Busan South Korea Univ Econ & Law Fac Informat Syst Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam

ISBN: (纸本)9798350318920;9798350318937

Although point cloud segmentation has a principal role in 3d understanding, annotating fully large-scale scenes for this task can be costly and time-consuming. To resolve this issue, we propose Point Central Transformer (PointCT), a novel end-to-end trainable transformer network for weakly-supervised point cloud semantic segmentation. divergent from prior approaches, our method addresses limited point annotation challenges exclusively based on 3d points through central-based attention. By employing two embedding processes, our attention mechanism integrates global features across neighborhoods, thereby effectively enhancing unlabeled point representations. Simultaneously, the interconnections between central points and their distinct neighborhoods are bidirectional cohered. Position encoding is further applied to enforce geometric features and improve overall performance. Notably, PointCT achieves outstanding performance under various labeled point settings without additional supervision. Extensive experiments on public datasets S3dIS, ScanNet-V2, and STPLS3d demonstrate the superiority of our proposed approach over other state-of-the-art methods.

关键词： 3d computer vision Algorithms

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An Efficient 3d data Annotation and Object detection Pipeline for Production Line

An Efficient 3D Data Annotation and Object Detection Pipelin...

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IEEE International Conference on Omni-Layer Intelligent Systems (IEEE COINS)

作者： Pansare, Pallavi Tripathi, ManMohan Gupta, Amit EInfochips Arrow Co Ahmadabad 380060 Gujarat India

ISBN: (纸本)9798350349603;9798350349597

2d (dimensional) object detection algorithms are important in computer vision and scene understanding. However, lacking depth perception in 2d images and the availability of low-cost 3d sensors attracted researchers' attention to 3d object detection. The 3d object detection approach is more accurate due to its 3d understanding and less susceptible to lighting variations. Nevertheless, it also has challenges like expensive computation costs, lack of training data, cumbersome data collection process etc. In this work, we tried to address these challenges to some extent by proposing an efficient 3d object detection pipeline, with an easy-to-use 3d data annotation methodology. We have also explored synthetic 3d data creation techniques to enrich the data. Also developed and deployed a computationally inexpensive 3d active region generator on an edge device to reduce the overall computational cost of our proposed pipeline. Our trained model achieves around 94% accuracy while processing data in real-time.

关键词： 3d computer vision Object detection NVIdIA Edge device Time-of-Flight(ToF) 3d-data annotation digital Twins

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Single Frame Semantic Segmentation Using Multi-Modal Spherical Images

Single Frame Semantic Segmentation Using Multi-Modal Spheric...

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IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Gutikonda, Suresh Rambach, Jason German Res Ctr Artificial Intelligence DFKI Kaiserslautern Germany

ISBN: (纸本)9798350318920;9798350318937

In recent years, the research community has shown a lot of interest to panoramic images that offer a 360 degrees directional perspective. Multiple data modalities can be fed, and complimentary characteristics can be utilized for more robust and rich scene interpretation based on semantic segmentation, to fully realize the potential. Existing research, however, mostly concentrated on pinhole RGB-X semantic segmentation. In this study, we propose a transformer-based cross-modal fusion architecture to bridge the gap between multi-modal fusion and omnidirectional scene perception. We employ distortion-aware modules to address extreme object deformations and panorama distortions that result from equirectangular representation. Additionally, we conduct cross-modal interactions for feature rectification and information exchange before merging the features in order to communicate long-range contexts for bi-modal and tri-modal feature streams. In thorough tests using combinations of four different modality types in three indoor panoramic-view datasets, our technique achieved state-of-the-art mIoU performance: 60.60% on Stanford2d3dS [2] (RGB-HHA), 71.97% on Structured3d [44] (RGB-d-N), and 35.92% on Matterport3d [5] (RGB-d). (1)

关键词： 3d computer vision Algorithms

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Context-based Interpretable Spatio-Temporal Graph Convolutional Network for Human Motion Forecasting

Context-based Interpretable Spatio-Temporal Graph Convolutio...

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IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Medina, Edgar Loh, Leyong Gurung, Namrata Oh, Kyung Hun Heller, Niels QualityMinds GmbH Berlin Germany

ISBN: (纸本)9798350318920;9798350318937

Human motion prediction is still an open problem extremely important for autonomous driving and safety applications. due to the complex spatiotemporal relation of motion sequences, this remains a challenging problem not only for movement prediction but also to perform a preliminary interpretation of the joint connections. In this work, we present a Context-based Interpretable Spatio-Temporal Graph Convolutional Network (CIST-GCN), as an efficient 3d human pose forecasting model based on GCNs that encompasses specific layers, aiding model interpretability and providing information that might be useful when analyzing motion distribution and body behavior. Our architecture extracts meaningful information from pose sequences, aggregates displacements and accelerations into the input model, and finally predicts the output displacements. Extensive experiments on Human 3.6M, AMASS, 3dPW, and ExPI datasets demonstrate that CIST-GCN outperforms previous methods in human motion prediction and robustness. Since the idea of enhancing interpretability for motion prediction has its merits, we showcase experiments towards it and provide preliminary evaluations of such insights here. (1)

关键词： 3d computer vision Algorithms Algorithms and algorithms Applications Autonomous driving formulations Machine learning architectures

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Multi-view 3d Object Reconstruction and Uncertainty Modelling with Neural Shape Prior

Multi-view 3D Object Reconstruction and Uncertainty Modellin...

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IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Liao, Ziwei Waslander, Steven L. Univ Toronto Robot Inst Toronto ON Canada Univ Toronto Inst Aerosp Study Toronto ON Canada

ISBN: (纸本)9798350318920;9798350318937

3d object reconstruction is important for semantic scene understanding. It is challenging to reconstruct detailed 3d shapes from monocular images directly due to a lack of depth information, occlusion and noise. Most current methods generate deterministic object models without any awareness of the uncertainty of the reconstruction. We tackle this problem by leveraging a neural object representation which learns an object shape distribution from large dataset of 3d object models and maps it into a latent space. We propose a method to model uncertainty as part of the representation and define an uncertainty-aware encoder which generates latent codes with uncertainty directly from individual input images. Further, we propose a method to propagate the uncertainty in the latent code to SdF values and generate a 3d object mesh with local uncertainty for each mesh component. Finally, we propose an incremental fusion method under a Bayesian framework to fuse the latent codes from multi-view observations. We evaluate the system in both synthetic and real datasets to demonstrate the effectiveness of uncertainty-based fusion to improve 3d object reconstruction accuracy.

关键词： 3d 3d computer vision Algorithms Algorithms Algorithms etc. Generative models for image Image recognition and understanding video

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