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Haptics-equiped interactive PCI simulation for patient-specific surgery training and rehearsing

Science China(Information Sciences) 2016年第10期59卷 233-238页

作者： Shuai LI Qing XIA Aimin HAO Hong QIN Qinping ZHAO State Key Laboratory of Virtual Reality Technology and Systems Beihang University Department of Computer Science Stony Brook University

Despite the long history of medical simulations, suffering from the patient-specific heterogeneous heart physiological structure and complex intravascular procedures, it is still challenging for patient-specific percutaneous coronary intervention(PCI) surgery simulation. In this paper, we advocate a haptics-equiped interactive prototype system towards PCI surgeons training and patient-specific surgery rehearsing, which can afford trainees the opportunity to approximately experience the entire PCI procedures and customized emergency cases that might occur in common clinical settings. The full simulation covers tissue deformation, catheter and wire simulation, X-ray simulation, haptic feedback, and 3D realistic rendering, which in all give rise to the integrated physical, visual, haptic, and procedural realism. Our system can accommodate various comprehensive operations involved in PCI-related procedures, including feeding wires, releasing stents, injecting contrast medium, simulating X-ray, bleeding, etc. Moreover, our system framework is fully built upon CUDA, and thus can achieve real-time performance even on a common desktop. The high-fidelity, real-time efficiency and stableness of our system show great potentials for its practical applications in clinical training fields.

关键词： percutaneous coronary intervention(PCI) PCI surgery simulation patient-specific surgery rehearsing physically-based modeling haptics

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AR assistance for efficient dynamic target search

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Computational Visual Media 2023年第1期9卷 177-194页

作者： Zixiang Zhao Jian Wu Lili Wang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijingChina

When searching for a dynamic target in an unknown real world scene,search efficiency is greatly reduced if users lack information about the spatial structure of the *** target search studies,especially in robotics,focus on determining either the shortest path when the target’s position is known,or a strategy to find the target as quickly as possible when the target’s position is ***,the target’s position is often known intermittently in the real world,e.g.,in the case of using surveillance *** goal is to help user find a dynamic target efficiently in the real world when the target’s position is intermittently *** order to achieve this purpose,we have designed an AR guidance assistance system to provide optimal current directional guidance to users,based on searching a prediction *** assume that a certain number of depth cameras are fixed in a real scene to obtain dynamic target’s *** system automatically analyzes all possible meetings between the user and the target,and generates optimal directional guidance to help the user catch up with the target.A user study was used to evaluate our method,and its results showed that compared to free search and a top-view method,our method significantly improves target search efficiency.

关键词： augmented reality(AR) search guidance

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Learning Semantic Concepts from Noisy Media Collection for Automatic Image Annotation

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Chinese Journal of Electronics 2015年第4期24卷 790-794页

作者： TIAN Feng SHEN Xukun School of Computer and Information Technology Northeast Petroleum University State Key Laboratory of Virtual Reality Technology and Systems Beihang University

Along with the explosive growth of images, automatic image annotation has attracted great interest of various research communities. However, despite the great progress achieved in the past two decades, automatic annotation is still an important open problem in computer vision, and can hardly achieve satisfactory performance in real-world environment. In this paper, we address the problem of annotation when noise is interfering with the dataset. A semantic neighborhood learning model on noisy media collection is proposed. Missing labels are replenished, and semantic balanced neighborhood is construct. The model allows the integration of multiple label metric learning and local nonnegative sparse *** construct semantic consistent neighborhood for each sample, thus corresponding neighbors have higher global similarity, partial correlation, conceptual similarity along with semantic balance. Meanwhile, an iterative denoising method is also proposed. The method proposed makes a marked improvement as compared to the current state-ofthe-art.

关键词： Image annotation Noisy media collection Semantic neighborhood

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Lighting virtual objects in a single image via coarse scene understanding

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Science China(Information Sciences) 2014年第9期57卷 58-71页

作者： CHEN XiaoWu JIN Xin WANG Ke State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang University

Achieving convincing visual consistency between virtual objects and a real scene mainly relies on the lighting effects of virtual-real composition *** problem becomes more challenging in lighting virtual objects in a single real ***,scene understanding from a single image has made great *** estimated geometry,semantic labels and intrinsic components provide mostly coarse information,and are not accurate enough to re-render the whole ***,carefully integrating the estimated coarse information can lead to an estimate of the illumination parameters of the real *** present a novel method that uses the coarse information estimated by current scene understanding technology to estimate the parameters of a ray-based illumination model to light virtual objects in a real *** key idea is to estimate the illumination via a sparse set of small 3D surfaces using normal and semantic *** coarse shading image obtained by intrinsic image decomposition is considered as the irradiance of the selected small *** virtual objects are illuminated by the estimated illumination *** results show that our method can convincingly light virtual objects in a single real image,without any pre-recorded 3D geometry,reflectance,illumination acquisition equipment or imaging information of the image.

关键词： illumination estimation lighting virtual object single image scene understanding photo editing

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Structure guided texture inpainting through multi-scale patches and global optimization for image completion

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Science China(Information Sciences) 2014年第1期57卷 25-40页

作者： CHEN XiaoWu ZHOU Bin GUO Yu XU Fang ZHAO QinPing State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University

Automatic image completion can provide convenient editing of consumer *** image completion methods find the best patch locally and then copy it to the hole region for texture *** the best patch is fixed in size,it is hard to adapt these methods efciently either to various patterns or to content ***,salient image structures can be estimated and propagated to guide the texture inpainting process for more plausible *** paper presents a novel image completion method using structure guided texture *** do not require any interactions to achieve automatic two-stage image *** the structure completion stage,the salient structures around the hole region are detected,and then the missing structure curves are completed with Euler *** textures along the structure curves are completed through belief *** the texture inpainting stage,we propose multi-scale patches and global optimization to inpaint the remaining texture in the hole regions guided by the completed ***,with defined patch sizes,the hole region is divided into lattice patches,making it possible for multiple patch sizes to render multiscale descriptions of this image.A multi-scale graph is then built for the hole region and formulated as a posterior probability ***,using a simulated annealing based Markov chain Monte Carlo method,an inference algorithm is designed to find a global optimization solution for the posterior probability *** experiments show that our method can automatically complete the hole region and preserve plausible structure shapes of existing ones in various *** texture inpainting results are more convincing with guidance from the completed structures,and our method can guarantee and accelerate convergence of the global optimization.

关键词： image completion texture inpainting structure propagation global optimization

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Modeling Garment Seam from a Single Image

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Journal of computer Science & technology 2018年第3期33卷 463-474页

作者： Chen-Xu Zhang Xiao-Wu Chen Hong-Yu Wu Bin Zhou State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing 100191 China

We propose an automatic garment seam modeling framework to create a garment model with the seam structure from a single image. In order to achieve this, a marked seam image database and parametric seam models have been set up. Given a real seam image, we first identify the type of the seam image based on our marked semn image database and the seam parameters are parsed automatically by our sewing thread estimation method. Second the seam initial model is generated through the pre-defined parametric seam models. A garment model with the seam structure is finally obtained based on the seam position infrmation which users have marked on the garment. Moreover. we verify the effectiveness of our method with numerous experiments.

关键词： image-based 3D modeling garment modeling garment seam

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Joint self-supervised and reference-guided learning for depth inpainting

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Computational Visual Media 2022年第4期8卷 597-612页

作者： Heng Wu Kui Fu Yifan Zhao Haokun Song Jia Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Depth information can benefit various computer vision tasks on both images and ***,depth maps may suffer from invalid values in many pixels,and also large *** improve such data,we propose a joint self-supervised and reference-guided learning approach for depth *** the self-supervised learning strategy,we introduce an improved spatial convolutional sparse coding module in which total variation regularization is employed to enhance the structural information while preserving edge *** module alternately learns a convolutional dictionary and sparse coding from a corrupted depth ***,both the learned convolutional dictionary and sparse coding are convolved to yield an initial depth map,which is effectively smoothed using local contextual *** reference-guided learning part is inspired by the fact that adjacent pixels with close colors in the RGB image tend to have similar depth *** thus construct a hierarchical joint bilateral filter module using the corresponding color image to fill in large *** summary,our approach integrates a convolutional sparse coding module to preserve local contextual information and a hierarchical joint bilateral filter module for filling using specific adjacent *** results show that the proposed approach works well for both invalid value restoration and large hole inpainting.

关键词： depth inpainting self-supervised learning reference-guided learning

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Monocular Video Guided Garment Simulation

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Journal of computer Science & technology 2015年第3期30卷 528-539页

作者：李发明陈小武周彬卢飞翔郭侃符强 State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing 100191 China

We present a prototype to generate a garment-shape sequence guided by a monocular video sequence. It is a combination of a physically-based simulation and a boundary-based modification. Given a garment in the video worn on a mannequin, the simulation generates a garment initial shape by exploiting the mannequin shapes estimated from the video. The modification then deforms the simulated 3D shape into such a shape that matches the garment 2D boundary extracted from the video. According to the matching correspondences between the vertices on the shape and the points on the boundary, the modification is implemented by attracting the matched vertices and their neighboring vertices. For best-matching correspondences and efficient performance, three criteria are introduced to select the candidate vertices for matching. Since modifying each garment shape independently may cause inter-frame oscillations, changes by the modification are also propagated from one frame to the next frame. As a result, the generated garment 3D shape sequence is stable and similar to the garment video sequence. We demonstrate the effectiveness of our prototype with a number of examples.

关键词： garment simulation monocular video shape correspondence

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Ore genesis and critical metal enrichment using deep learning algorithms in Gunga Pb-Zn deposit, Southern Pakistan: Constraints from sphalerite geochemistry and isotopic compositions

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Journal of Geochemical Exploration 2025年 275卷

作者： Gul, Muhammad A. Zhang, Huishan Yang, Yu Ren, Guangli Sun, Chao Zhao, Xiaojian Yousuf, Tahir Bin Shah, Ahmed Awan, Rizwan Sarwar Faisal, Mohamed Yang, Xiaoyong CAS Key Laboratory of Crust-Mantle Materials and Environments University of Science and Technology of China Hefei230026 China CAS Center for Excellence in Comparative Planetology University of Science and Technology of China Hefei230026 China MNR Key Laboratory for the Study of Focused Magmatism and Giant Ore Deposits Xi'an Center of Geological Survey CGS Xi'an710054 China Department of Computer Science and IT Virtual University Lahore Pakistan Center of Excellence in Mineralogy University of Baluchistan Quetta Pakistan School of Environment and Resources Engineering Hefei University of Technology Hefei China Department of Geology Faculty of Science Suez Canal University El-Ismailia41522 Egypt School of Geosciences and Info-Physics Central South University Changsha410083 China

The Kirthar Fold and Thrust Belt in southern Pakistan is widely recognized as a major metallogenic province owing to the extensive presence of Pb-Zn deposits within the Jurassic sedimentary shelf sequence. One of these deposits, the Gunga Pb-Zn deposit, is a significant economic ore deposit situated within the Anjira Formation and comprises two mineralization zones, the Upper Mineralization Zone (UMZ) and Lower Mineralization Zone (LMZ). Despite the scarcity of documentation on the metallogenic source, origin, mineralization temperature, and other physicochemical conditions, our research is the first to explore the metallogenic differentiation and ore genesis of the deposit by examining minor and trace elements in sphalerite and isotopic analyses (S-Pb). The Gunga Pb-Zn deposit, amounting to 6.86 Mt. with 2.1 % and 11.4 % Pb and Zn, respectively, is linked to the interbedded limestone and shale unit of the Anjira Formation, and approximately below the depth of 200–250 m of the Jurassic-Cretaceous contact, the mineralization is believed to occur in the upper section of the Formation. Sphalerite is the dominant ore mineral, galena is a subordinate mineral, and pyrite is present in the main ore minerals. Sphalerite in the LMZ was found in a vein style, whereas it was disseminated in the UMZ and enriched in (medians) Ge (166 ppm), Cd (541 ppm), Pb (139 ppm), Ag (42 ppm), and Hg (68 ppm), lacking In (0.17 ppm), Co (6 ppm), and Mn (19). Based on trace element concentrations, the LMZ has a reduced environment, higher sulfur fugacity, and higher mineralization temperature (180–200 °C) than the UMZ, which has a lower mineralization temperature (130–180 °C). The presence of black shales, low temperatures, and high sulfur fugacity are the key parameters for Ge enrichment in the Gunga deposit. The δ34S values range from −10 to +5.6 ‰, suggesting that reduced sulfur was produced by both the BSR and TSR mechanisms, with basinal brines and seawater being the main sources of sulfur

关键词： Sulfur

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3D Dental Model Segmentation with Geometrical Boundary Preserving

arXiv

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arXiv 2025年

作者： Xi, Shufan Liu, Zexian Chang, Junlin Wu, Hongyu Wang, Xiaogang Hao, Aimin State Key Laboratory of Virtual Reality Technology and Systems Beihang University China College of Computer and Information Science Southwest University China Peng Cheng Laboratory China

3D intraoral scan mesh is widely used in digital dentistry diagnosis, segmenting 3D intraoral scan mesh is a critical preliminary task. Numerous approaches have been devised for precise tooth segmentation. Currently, the deep learning-based methods are capable of the high accuracy segmentation of crown. However, the segmentation accuracy at the junction between the crown and the gum is still below average. Existing down-sampling methods are unable to effectively preserve the geometric details at the junction. To address these problems, we propose CrossTooth, a boundary-preserving segmentation method that combines 3D mesh selective downsampling to retain more vertices at the tooth-gingiva area, along with cross-modal discriminative boundary features extracted from multi-view rendered images, enhancing the geometric representation of the segmentation network. Using a point network as a backbone and incorporating image complementary features, CrossTooth significantly improves segmentation accuracy, as demonstrated by experiments on a public intraoral scan dataset. The source code is available at https://***/XiShuFan/CrossTooth_CVPR2025 Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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