Computers & Graphics-Uk最新文献

This study introduces a novel framework for choreographing multi-degree of freedom (MDoF) behaviors in large-scale crowd simulations. The framework integrates multi-objective optimization with spatio-temporal ordering to effectively generate and control diverse MDoF crowd behavior states. We propose a set of evaluation criteria for assessing the aesthetic quality of crowd states and employ multi-objective optimization to produce crowd states that meet these criteria. Additionally, we introduce time offset functions and interpolation progress functions to perform complex and diversified behavior state interpolations. Furthermore, we designed a user-centric interaction module that allows for intuitive and flexible adjustments of crowd behavior states through sketching, spline curves, and other interactive means. Qualitative tests and quantitative experiments on the evaluation criteria demonstrate the effectiveness of this method in generating and controlling MDoF behaviors in crowds. Finally, case studies, including real-world applications in the Opening Ceremony of the 2022 Beijing Winter Olympics, validate the practicality and adaptability of this approach.

Ultrasound imaging, characterized by low contrast, high noise, and interference from surrounding tissues, poses significant challenges in lesion segmentation. To tackle these issues, we introduce an enhanced U-shaped network that incorporates several novel features for precise, automated segmentation. Firstly, our model utilizes a convolution-based self-attention mechanism to establish long-range dependencies in feature maps, crucial for small dataset applications, accompanied by a soft thresholding method for noise reduction. Secondly, we employ multi-sized convolutional kernels to enrich feature processing, coupled with curvature calculations to accentuate edge details via a soft-attention approach. Thirdly, an advanced skip connection strategy is implemented in the UNet architecture, integrating information entropy to assess and utilize texture-rich channels, thereby improving semantic detail in the encoder before merging with decoder outputs. We validated our approach using a newly curated dataset, VPUSI (Vascular Plaques Ultrasound Images), alongside the established datasets, BUSI, TN3K and DDTI. Comparative experiments on these datasets show that our model outperforms existing state-of-the-art techniques in segmentation accuracy.

The ShapeBench evaluation methodology is proposed as an extension to the popular Area Under Precision-Recall Curve (PRC/AUC) for measuring the matching performance of local 3D shape descriptors. It is observed that the PRC inadequately accounts for other similar surfaces in the same or different objects when determining whether a candidate match is a true positive. The novel Descriptor Distance Index (DDI) metric is introduced to address this limitation. In contrast to previous evaluation methodologies, which identify entire objects in a given scene, the DDI metric measures descriptor performance by analysing point-to-point distances. The ShapeBench methodology is also more scalable than previous approaches, by using procedural generation. The benchmark is used to evaluate both old and new descriptors. The results produced by the implementation of the benchmark are fully replicable, and are made publicly available.

Recently, graph-based and Transformer-based deep learning have demonstrated excellent performances on various point cloud tasks. Most of the existing graph-based methods rely on static graph, which take a fixed input to establish graph relations. Moreover, many graph-based methods apply maximizing and averaging to aggregate neighboring features, so that only a single neighboring point affects the feature of centroid or different neighboring points own the same influence on the centroid’s feature, which ignoring the correlation and difference between points. Most Transformer-based approaches extract point cloud features based on global attention and lack the feature learning on local neighbors. To solve the above issues of graph-based and Transformer-based models, we propose a new feature extraction block named Graph Transformer and construct a 3D point cloud learning network called GTNet to learn features of point clouds on local and global patterns. Graph Transformer integrates the advantages of graph-based and Transformer-based methods, and consists of Local Transformer that use intra-domain cross-attention and Global Transformer that use global self-attention. Finally, we use GTNet for shape classification, part segmentation and semantic segmentation tasks in this paper. The experimental results show that our model achieves good learning and prediction ability on most tasks. The source code and pre-trained model of GTNet will be released on https://github.com/NWUzhouwei/GTNet.

Skin flaps are common procedures used by surgeons to cover an excised area during the reconstruction of a defect. It is often a challenging task for a surgeon to come up with the most optimal design for a patient. In this paper, we set up a simulation system based on the finite element method for one of the most common flap types — the rhomboid flap. Instead of using the standard 2D planar patch, we constructed a 3D patch with multiple layers. This allowed us to investigate the impact of different undermining areas and depths. We compared the suture forces for each case and identified vertices with the largest suture force. The shape of the final suture line is also visualized for each case, which is an important clue when deciding on the most optimal skin flap orientation according to medical textbooks. We found that under the optimal undermining setup, the maximum suture force is around 0.7 N for top of the undermined layer and 1.0 N for bottom of the undermined layer. When measuring difference in final suture line shape, the maximum normalized Hausdorff distance is 0.099, which suggests that different undermining region can have significant impact on the shape of the suture line, especially in the tail region. After analyzing the suture force plots, we provided recommendations on the most optimal undermining region for rhomboid flaps.

Computer-aided design (CAD) tools are utilized in the manufacturing industry for modeling everything from cups to spacecraft. These programs are complex to use and typically require years of training and experience to master. Structured and well-constrained 2D sketches and 3D constructions are crucial components of CAD modeling. A well-executed CAD model can be seamlessly integrated into the manufacturing process, thereby enhancing production efficiency. Deep generative models of 3D shapes and 3D object reconstruction models have garnered significant research interest. However, most of these models produce discrete forms of 3D objects that are not editable. Moreover, the few models based on CAD operations often have substantial input restrictions. In this work, we fine-tuned pre-trained models to create OpenECAD models (0.55B, 0.89B, 2.4B and 3.1B), leveraging the visual, logical, coding, and general capabilities of visual language models. OpenECAD models can process images of 3D designs as input and generate highly structured 2D sketches and 3D construction commands, ensuring that the designs are editable. These outputs can be directly used with existing CAD tools’ APIs to generate project files. To train our network, we created a series of OpenECAD datasets. These datasets are derived from existing public CAD datasets, adjusted and augmented to meet the specific requirements of vision language model (VLM) training. Additionally, we have introduced an approach that utilizes dependency relationships to define and generate sketches, further enriching the content and functionality of the datasets.

In recent years, remarkable ability has been demonstrated by the Transformer model in capturing remote dependencies and improving point cloud segmentation performance. However, localized regions separated from conventional sampling architectures have resulted in the destruction of structural information of instances and a lack of exploration of potential geometric relationships between localized regions. To address this issue, a Local Spatial Latent Geometric Relation Learning Network (LSGRNet) is proposed in this paper, with the geometric properties of point clouds serving as a reference. Specifically, spatial transformation and gradient computation are performed on the local point cloud to uncover potential geometric relationships within the local neighborhood. Furthermore, a local relationship aggregator based on semantic and geometric relationships is constructed to enable the interaction of spatial geometric structure and information within the local neighborhood. Simultaneously, boundary interaction feature learning module is employed to learn the boundary information of the point cloud, aiming to better describe the local structure. The experimental results indicate that excellent segmentation performance is exhibited by the proposed LSGRNet in benchmark tests on the indoor datasets S3DIS and ScanNetV2, as well as the outdoor datasets SemanticKITTI and Semantic3D.

Convolutional Neural Networks (CNNs) have proven highly effective for demosaicking, transforming raw Color Filter Array (CFA) sensor samples into standard RGB images. Directly applying convolution to the CFA tensor can lead to misinterpretation of the color context, so existing demosaicking networks typically embed the CFA tensor into the Euclidean space before convolution. The most prevalent embedding options are Reordering and Pre-interpolation. However, it remains unclear which option is more advantageous for demosaicking. Moreover, no existing demosaicking network is suitable for conducting a fair comparison. As a result, in practice, the selection of these two embedding options is often based on intuition and heuristic approaches. This paper addresses the non-comparability between the two options and investigates whether pre-interpolation contributes additional knowledge to the demosaicking network. Based on rigorous mathematical derivation, we design pairs of end-to-end fully convolutional evaluation networks, ensuring that the performance difference between each pair of networks can be solely attributed to their differing CFA embedding strategies. Under strictly fair comparison conditions, we measure the performance contrast between the two embedding options across various scenarios. Our comprehensive evaluation reveals that the prior knowledge introduced by pre-interpolation benefits lightweight models. Additionally, pre-interpolation enhances the robustness to imaging artifacts for larger models. Our findings offer practical guidelines for designing imaging software or Image Signal Processors (ISPs) for RGB cameras.