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FDCPNet:feature discrimination and context propagation network for 3D shape representation

虚拟现实与智能硬件(中英文) 2025年第1期7卷 83-94页

作者： Weimin SHI Yuan XIONG Qianwen WANG Han JIANG Zhong ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation *** methods for extracting features from mesh edges or faces struggle with complex 3D models because edge-based approaches miss global contexts and face-based methods overlook variations in adjacent areas,which affects the overall *** address these issues,we propose the Feature Discrimination and Context Propagation Network(FDCPNet),which is a novel approach that synergistically integrates local and global features in mesh *** FDCPNet is composed of two modules:(1)the Feature Discrimination Module,which employs an attention mechanism to enhance the identification of key local features,and(2)the Context Propagation Module,which enriches key local features by integrating global contextual information,thereby facilitating a more detailed and comprehensive representation of crucial areas within the mesh *** Experiments on popular datasets validated the effectiveness of FDCPNet,showing an improvement in the classification accuracy over the baseline ***,even with reduced mesh face numbers and limited training data,FDCPNet achieved promising results,demonstrating its robustness in scenarios of variable complexity.

关键词： 3D shape representation Mesh model MeshNet Feature discrimination Context propagation

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kLCRNet: Fast Road Network Extraction Via keypoint-Driven Local Connectivity Exploration

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12074-12089页

作者： Zhang, Mingming Wang, Bin Yang, Shuai Liu, Qingjie Wang, Yunhong Beihang University Hangzhou Innovation Institute Hangzhou310051 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China

Road network extraction from remote sensing images has been extensively studied in recent decades. While many approaches output road networks in vector format, most are not fully end-to-end, requiring time-consuming post-processing steps. Additionally, challenges like isomorphic encoding limit the flexibility of these methods. In this paper, we present kLCRNet, an efficient road network extraction framework that overcomes these limitations by leveraging keypoint-driven local connectivity exploration. kLCRNet consists of two key components: a keypoint detection (KPD) module that identifies road keypoints via heatmap-based detection and refines them using bipartite matching, and a local connectivity exploration (LCE) module that samples local connection relationships to directly construct connectivity between detected keypoints. Experiments on the CityScale and SpaceNet datasets demonstrate that kLCRNet outperforms state-of-the-art (SOTA) methods in topological accuracy and connectivity. Additionally, kLCRNet significantly improves inference speed by up to 25 times, highlighting its efficiency and effectiveness. © 2008-2012 IEEE.

关键词： Roads Accuracy Remote sensing Feature extraction Vectors Iterative methods Image segmentation Semantic segmentation Encoding Decoding

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Saliency Based Data Augmentation for Few-Shot Video Action Recognition 31st

Saliency Based Data Augmentation for Few-Shot Video Action ...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Kong, Yongqiang Wang, Yunhong Li, Annan The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

ISBN: (纸本)9789819620630

Despite the progress made in few-shot video action recognition, existing methods still struggle to achieve satisfactory performance when support samples are limited (e.g., 1-shot task). This paper proposes to augment training samples without relying on additional supervision and labor costs, aiming at improving generalizability of learned representations. We introduce a novel self-supervised salient object detection model which results in frame-level saliency and background features of videos. A shared encoder is employed to fuse saliency and background information from different videos. Both intra- and inter-class fusion are performed, in which the latter is controlled by prior probability to avoid semantic ambiguities. This way actually corresponds to augment training data in feature space. The saliency-background representations formed from query and support videos are used to construct class prototypes through Temporal-Relational CrossTransformers. Experimental results on four standard benchmarks demonstrate that the proposed method outperforms state-of-the-arts under various few-shot settings, particularly excelling in the 1-shot case. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wages

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PwP: Permutating with Probability for Efficient Group Selection in VR

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IEEE Transactions on Visualization and Computer Graphics 2025年第5期31卷 2384-2394页

作者： Wu, Jian Zhang, Weicheng Chen, Handong Lin, Wei Shi, Xuehuai Wang, Lili Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Nanjing University of Posts and Telecommunications China Peng Cheng Laboratory Shenzhen China

Group selection in virtual reality is an important means of multi-object selection, which allows users to quickly group multiple objects and can significantly improve the operation efficiency of multiple types of objects. In this paper, we propose a group selection method based on multiple rounds of probability permutation, in which the efficiency of group selection is substantially improved by making the object layout of the next round easier to be batch-selected through interactive selection, object grouping probability computation, and position rearrangement in each round of the selection process. We conducted ablation experiments to determine the algorithm coefficients and validate the effectiveness of the algorithm. In addition, an empirical user study was conducted to evaluate the ability of our method to significantly improve the efficiency of the group selection task in an immersive virtual reality environment. The reduced operations also indirectly reduce the user task load and improve usability. © 1995-2012 IEEE.

关键词： virtual environments

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Fov-GS: Foveated 3D Gaussian Splatting for Dynamic Scenes

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IEEE Transactions on Visualization and Computer Graphics 2025年第5期31卷 2975-2985页

作者： Fan, Runze Wu, Jian Shi, Xuehuai Zhao, Lizhi Ma, Qixiang Wang, Lili Nanjing University of Posts and Telecommunications China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Peng Cheng Laboratory Shenzhen China

Rendering quality and performance greatly affect the user's immersion in VR experiences. 3D Gaussian Splatting-based methods can achieve photo-realistic rendering with speeds of over 100 fps in static scenes, but the speed drops below 10 fps in monocular dynamic scenes. Foveated rendering provides a possible solution to accelerate rendering without compromising visual perceptual quality. However, 3DGS and foveated rendering are not compatible. In this paper, we propose Fov-GS, a foveated 3D Gaussian splatting method for rendering dynamic scenes in real time. We introduce a 3D Gaussian forest representation that represents the scene as a forest. To construct the 3D Gaussian forest, we propose a 3D Gaussian forest initialization method based on dynamic-static separation. Subsequently, we propose a 3D Gaussian forest optimization method based on deformation field and Gaussian decomposition to optimize the forest and deformation field. To achieve real-time dynamic scene rendering, we present a 3D Gaussian forest rendering method based on HVS models. Experiments demonstrate that our method not only achieves higher rendering quality in the foveal and salient regions compared to the SOTA methods but also dramatically improves rendering performance, achieving up to 11.33X speedup. We also conducted a user study, and the results prove that the perceptual quality of our method has a high visual similarity with the ground truth. © 1995-2012 IEEE.

关键词： Digital elevation model

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Where Should a virtual Guide Stand in a VR Museum? 1st

Where Should a Virtual Guide Stand in a VR Museum?

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1st International Conference on Extended reality, ICXR 2024

作者： Liu, Xinda Jiang, Kun Wu, Jian Wang, Lili Geng, Guohua Northwest University Shanxi710127 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

ISBN: (纸本)9789819636785

In VR museums, where virtual guides play a key role, optimizing their position is critical to enhancing user experience. This paper models the location relationship between relic, visitor and virtual guide through user studies, and introduces the Asymmetrical Mutual virtual Retargeting (AMVR) method, which addresses challenges related to visual occlusion and multi-user sensory inconsistency. AMVR uses a unique virtual retargeting technique applied to both users simultaneously. The method redirects each user’s gaze to the other’s guide, while confusing the direction of view and body orientation, so that each user believes everyone is looking at their guide. This approach optimizes the guide’s position based on the differing locations of users, resulting in asymmetrical rotational gains during retargeting. By improving the spatial interaction between users and virtual guides, AMVR ensures smoother, more comfortable navigation while maintaining consistent guide presence for all users. Two user studies conducted to evaluate the method demonstrated significant improvements in task efficiency and user satisfaction, particularly in reducing occlusion and enhancing the perception of the consistent guide. Questionnaire results showed AMVR did not increase user discomfort and provided a more intuitive experience compared to other methods. AMVR offers a promising solution for optimizing multi-user interactions in VR museums, providing a strong basis for future research in virtual human-computer interaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： virtual environments

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Identification of Gingival Inflammation Surface Image Features Using Intraoral Scanning and Deep Learning

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International Dental Journal 2025年第3期75卷 2104-2114页

作者： Li, Wei Li, Linlin Xu, Wenchong Guo, Yuting Xu, Min Huang, Shengyuan Dai, Dong Lu, Chang Li, Shuai Lin, Jiang Department of Stomatology Beijing Tongren Hospital Capital Medical University Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

Introduction and aim: The assessment of gingival inflammation surface features mainly depends on subjective judgment and lacks quantifiable and reproducible indicators. Therefore, it is a need to acquire objective identification information for accurate monitoring and diagnosis of gingival inflammation. This study aims to develop an automated method combining intraoral scanning (IOS) and deep learning algorithms to identify the surface features of gingival inflammation and evaluate its accuracy and correlation with clinical indicators. Methods: The study included the periodontal probing data and intraoral scan images of 120 patients with periodontitis. The deep learning model GC-U-Net was used to automatically segment and identify the gingival inflammation regions. The performance of the model was evaluated by the Dice coefficient (Dice), intersection over union (IoU), and pixel accuracy (PA), and the correlation between the identification performance and the periodontal examination index was analysed. Results: The GC-U-Net model showed high recognition accuracy, with a Dice of 77.8%, an IoU of 65.4%, and a PA of 93.7%. This model demonstrated a strong positive correlation with the sulcus bleeding index (SBI;r = 0.836, P < .001), a moderately strong positive correlation with the bleeding index (BI;r = 0.618, P < .001), and a negative correlation with the probe depth (PD;r = - 0.425, P < .001). Conclusion: The study has successfully developed an automatic identification method for surface characteristics of gingival inflammation based on deep learning and IOS technology, providing a standardised and automated auxiliary tool for clinical gingival inflammation examination with high accuracy and significant correlation with clinical indicators. Clinical Relevance: This method can reduce subjective judgment in the clinical assessment of gingival inflammation, improve the consistency and reliability of identification, and play an important auxiliary role in clinical diagn

关键词： Artificial intelligence Deep learning Gingival inflammation Intraoral scanning Periodontitis

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Subthreshold Depression Recognition and Correlation Study from Pulse Condition via Stacking Ensemble Algorithm 1st

Subthreshold Depression Recognition and Correlation Study f...

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1st International Conference on Extended reality, ICXR 2024

作者： Jiang, Han Li, Ming Gao, Yang Li, Peiru State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9789819636785

Subthreshold depression, a transition state to depression, seriously hinders the early diagnosis of depression. Current studies mostly use heterogeneous definitions of subthreshold depression, making the results of such meta-analyses questionable. Therefore, it is of vital significance to develop an objective method for the diagnosis of subthreshold depression based on objective criteria. In traditional Chinese medicine (TCM), symptoms similar to subthreshold depression have been extensively explored. However, diagnostic methods in TCM still depend heavily on the experience of doctors and lack integration with modern diagnostic techniques, which makes it challenging to explain the pathogenesis of subthreshold depression. Consequently, we propose an explainable framework, based on a stacking ensemble algorithm, for subthreshold depression recognition from biomarkers in the pulse waveform and concepts of pulse in TCM. In this method, Naive Bayes, Random Forest, Extremely Randomized Trees, Categorical Boosting and Logistic Regression are chosen as basic learners, and XGBoost is selected as the meta-classifier. Based on the five-fold cross-validation method, grid search method and repetition of training, the stacking ensemble model shows superiority on most performance evaluation metrics including AUC, F1 scores, MCC, precision and sensitivity. Besides, by analyzing the Adjusted Odds Ratio of features in the pulse waveform, we obtained four features that have a high correlation with the occurrence of subthreshold depression and derived physiological changes in patients with subthreshold depression based on their physiological significance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diagnosis

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Fish Detection and Quantity Estimation Based on Sonar Images 1st

Fish Detection and Quantity Estimation Based on Sonar Images

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1st International Conference on Extended reality, ICXR 2024

作者： Du, Wenxiang Yan, Shuai Qi, Yue State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Qingdao Research Institute of Beihang University Qingdao266100 China

ISBN: (纸本)9789819636785

Due to the complex underwater environment and dense targets, it is difficult to quickly and accurately calculate the location and quantity of fish targets in marine ranching. We have developed an underwater target detection and counting algorithm based on sonar images, which includes four parts: image preprocessing, background subtraction, contour detection, and target counting. Firstly, we design a sliding-window-based gain algorithm based on the uneven grayscale value of sonar images, which amplifies the effective signal while smoothing the grayscale of the image. Secondly, background subtraction is used to separate the foreground and background of the sonar image, which can remove background noise and filter the target using a filtering algorithm to generate a binary image with clear targets. Then, target detection is combined with morphological processing techniques and contour detection algorithms. Finally, we use image erosion technology to separate overlapping targets, calculate the contour position of the targets, and count the quantity. A large number of results indicate that our algorithm can quickly and accurately detect the position of fish in sonar images while also counting the number of fishes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Binary images

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Segmentation of CAD models using hybrid representation

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虚拟现实与智能硬件(中英文) 2025年第2期7卷 188-202页

作者： Claude UWIMANA Shengdi ZHOU Limei YANG Zhuqing LI Norbelt MUTAGISHA Edouard NIYONGABO Bin ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peace-Bay Academy of Virtual Reality Shenyang 110004China China Aerospace Systems Engineering Corporation Beijing 100070China Department of Signal and Information Processing Beihang UniversityBeijing 100191China Department of Computer Science University of SherbrookeSherbrooke J1C0A1Canada

In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD *** previous CAD segmentation methods have achieved impressive performance using single representations,such as meshes,CAD,and point ***,existing methods cannot effectively combine different three-dimensional model types for the direct conversion,alignment,and integrity maintenance of geometric and topological ***,we propose an integration approach that combines the geometric accuracy of CAD data with the flexibility of mesh representations,as well as introduce a unique hybrid representation that combines CAD and mesh models to enhance segmentation *** combine these two model types,our hybrid system utilizes advanced-neural-network techniques to convert CAD models into mesh *** complex CAD models,model segmentation is crucial for model retrieval and *** partial retrieval,it aims to segment a complex CAD model into several simple *** first component of our hybrid system involves advanced mesh-labeling algorithms that harness the digitization of CAD properties to mesh *** second component integrates labelled face features for CAD segmentation by leveraging the abundant multisemantic information embedded in CAD *** combination of mesh and CAD not only refines the accuracy of boundary delineation but also provides a comprehensive understanding of the underlying object *** study uses the Fusion 360 Gallery *** results indicate that our hybrid method can segment these models with higher accuracy than other methods that use single representations.

关键词： B-RepNet hybrid segmentation CAD models classification MeshCNN MeshCAD-Net

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