A Parallel Feature-preserving Mesh Variable Offsetting Method with Dynamic Programming

Mesh offsetting plays an important role in discrete geometric processing. In this paper, we propose a parallel feature-preserving mesh offsetting framework with variable distance. Different from the traditional method based on distance and normal vector, a new calculation of offset position is proposed by using dynamic programming and quadratic programming, and the sharp feature can be preserved after offsetting. Instead of distance implicit field, a spatial coverage region represented by polyhedral for computing offsets is proposed. Our method can generate an offsetting model with smaller mesh size, and also can achieve high quality without gaps, holes, and self-intersections. Moreover, several acceleration techniques are proposed for the efficient mesh offsetting, such as the parallel computing with grid, AABB tree and rays computing. In order to show the efficiency and robustness of the proposed framework, we have tested our method on the quadmesh dataset, which is available at [https://www.quadmesh.cloud]. The source code of the proposed algorithm is available on GitHub at [https://github.com/iGame-Lab/PFPOffset]

TTMFN: Two-stream Transformer-based Multimodal Fusion Network for Survival Prediction

Survival prediction plays a crucial role in assisting clinicians with the development of cancer treatment protocols. Recent evidence shows that multimodal data can help in the diagnosis of cancer disease and improve survival prediction. Currently, deep learning-based approaches have experienced increasing success in survival prediction by integrating pathological images and gene expression data. However, most existing approaches overlook the intra-modality latent information and the complex inter-modality correlations. Furthermore, existing modalities do not fully exploit the immense representational capabilities of neural networks for feature aggregation and disregard the importance of relationships between features. Therefore, it is highly recommended to address these issues in order to enhance the prediction performance by proposing a novel deep learning-based method. We propose a novel framework named Two-stream Transformer-based Multimodal Fusion Network for survival prediction (TTMFN), which integrates pathological images and gene expression data. In TTMFN, we present a two-stream multimodal co-attention transformer module to take full advantage of the complex relationships between different modalities and the potential connections within the modalities. Additionally, we develop a multi-head attention pooling approach to effectively aggregate the feature representations of the two modalities. The experiment results on four datasets from The Cancer Genome Atlas demonstrate that TTMFN can achieve the best performance or competitive results compared to the state-of-the-art methods in predicting the overall survival of patients

Towards Multi-Person 3D Pose Estimation in Natural Videos

Thesis (Ph.D.)--University of Washington, 2020Despite the increasing need of analyzing human poses on the street and in the wild, multi-person 3D pose estimation using static or moving monocular camera in real-world scenarios remains a challenge, requiring large-scale training data or high computation complexity due to the high degrees of freedom in 3D human poses. To address these challenges, a novel scheme, Hierarchical 3D Human Pose Estimation (H3DHPE), is proposed to effectively track and hierarchically estimate 3D human poses in natural videos in an efficient fashion. Torso estimation is formulated as a Perspective-N-Point (PNP) problem, limb pose estimation is solved as an optimization problem, and the high dimensional pose estimation is hierarchically addressed efficiently. As an extension to Hierarchical 3D Human Pose Estimation (H3DHPE), Universal Hierarchical 3D Human Pose Estimation (UH3DHPE) is proposed to handle the case of an occluded or inaccurate 2D torso keypoints, which makes torso-first estimation in H3DHPE unreliable. An effective method to directly estimate limb poses without building upon the estimated torso pose is proposed, and the torso pose can then be further refined to form the hierarchy in a bottom-up fashion. An adaptive merging strategy is proposed to determine the best hierarchy. The advantages of the proposed unsupervised methods are validated on various datasets including a lot of natural real-world scenes. For better evaluation and future research, a unique dataset called Moving camera Multi-Human interactions (MMHuman) is collected, with accurate MoCap ground truth, for multi-person interaction scenarios recorded by a monocular moving camera. Superior performance is shown on the newly collected MMHuman compared to state-of-the-art methods, including supervised methods, proving that our unsupervised solution generalize better to natural videos. To further tackle the problem of long term occlusions, a deep neutral network (DNN) solution is explored for trajectory recovery. To our best knowledge, it’s the first to use temporal gated convolutions to recover missing poses and address the occlusion issues in the pose estimation. A simple yet effective approach is proposed to transform normalized poses to the global trajectory into the camera coordinate

VTP: Volumetric Transformer for Multi-view Multi-person 3D Pose Estimation

This paper presents Volumetric Transformer Pose estimator (VTP), the first 3D volumetric transformer framework for multi-view multi-person 3D human pose estimation. VTP aggregates features from 2D keypoints in all camera views and directly learns the spatial relationships in the 3D voxel space in an end-to-end fashion. The aggregated 3D features are passed through 3D convolutions before being flattened into sequential embeddings and fed into a transformer. A residual structure is designed to further improve the performance. In addition, the sparse Sinkhorn attention is empowered to reduce the memory cost, which is a major bottleneck for volumetric representations, while also achieving excellent performance. The output of the transformer is again concatenated with 3D convolutional features by a residual design. The proposed VTP framework integrates the high performance of the transformer with volumetric representations, which can be used as a good alternative to the convolutional backbones. Experiments on the Shelf, Campus and CMU Panoptic benchmarks show promising results in terms of both Mean Per Joint Position Error (MPJPE) and Percentage of Correctly estimated Parts (PCP). Our code will be available

Visualizing Head and Neck Tumors in Vivo Using Near-Infrared Fluorescent Transferrin Conjugate

Transferrin receptor (TfR) is overexpressed in human head and neck squamous cell carcinomas (HNSCCs). This study was carried out to investigate the feasibility of imaging HNSCC by targeting TfR using near-infrared fluorescent transferrin conjugate (TfNIR). Western blot analysis of four HNSCC cell lines revealed overexpression of TfR in all four lines compared with that in normal keratinocytes (OKFL). Immunocytochemistry further confirmed the expression of TfR and endocytosis of TfNIR in JHU-013 culture cells. Following intravenous administration of TfNIR (200 μL, 0.625 μg/μL), fluorescent signal was preferentially accumulated in JHU-013 tumor xenografts grown in the lower back (n = 14) and oral base tissues (n = 4) of nude mice. The signal in tumors was clearly detectable as early as 10 minutes and reached the maximum at 90 to 120 minutes postinjection. The background showed an increase, followed by a decrease at a much faster pace than tumor signal. A high fluorescent ratio of the tumor to muscle was obtained (from 1.42 to 4.15 among tumors), usually achieved within 6 hours, and correlated with the tumor size (r = .74, p = .002). Our results indicate that TfR is a promising target and that TfNIR-based optical imaging is potentially useful for noninvasive detection of early HNSCC in the clinic

IGA-Reuse-NET: A deep-learning-based isogeometric analysis-reuse approach with topology-consistent parameterization[Formula presented]

In this paper, a deep learning framework combined with isogeometric analysis (IGA for short) called IGA-Reuse-Net is proposed for efficient reuse of numerical simulation on a set of topology-consistent models. Compared with previous data-driven numerical simulation methods only for simple computational domains, our method can predict high-accuracy PDE solutions over topology-consistent geometries with complex boundaries. UNet3+ architecture with interlaced sparse self-attention (ISSA) module is used to enhance the performance of the network. In addition, we propose a new loss function that combines a coefficients loss and a numerical solution loss. Several training datasets with topology-consistent models are constructed for the proposed framework. To verify the effectiveness of our approach, two different types of Poisson equations with different source functions are solved on three datasets with different topologies. Our framework can achieve a good trade-off between accuracy and efficiency. It outperforms the physics-informed neural network (PINN for short) model and yields promising results of prediction.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Materials and Manufacturin