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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2023年 9卷 210-223页

作者： Kumar, Ajeet Giusti, Elisa Mancuso, Francesco Ghio, Selenia Lupidi, Alberto Martorella, Marco Natl Lab CNIT Radar & Surveillance Syst RaSS I-56124 Pisa Italy Univ Pisa Dept Informat Engn I-56122 Pisa Italy

A new Polarimetric Interferometry Inverse Synthetic Aperture Radar (Pol-InISAR) 3d imaging method for non-cooperative targets is proposed in this paper. 3d imaging of non-cooperative targets becomes possible by combining additional information of interferometric phase along with conventional 2d ISAR imaging. In the previously reported single-polarimetry InISAR based 3d imaging, only a single-channel based interferometric phase is available that can be exploited to reconstruct the 3d ISAR image. This limits the ability to obtain a full target's scattering response and therefore limits the estimation of an accurate interferometric phase. To overcome this constraint, full-polarimetry information is being exploited in this paper, which allows to select the optimal polarimetric combination through which the highest coherence can be obtained. A higher coherence leads to a reduction (optimally a minimization) of the phase estimation error. Consequently, with an optimal phase estimation, an accurate 3d imaging of the target is possible. To validate this proposed Pol-InISAR based 3d imaging approach, both simulated and real datasets are taken under consideration.

关键词： Three-dimensional displays Imaging Coherence Optimization Radar imaging Interferometry Scattering ISAR pol-InISAR polarimetry interferometry In-SAR 3d imaging non-cooperative target Tank-72

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Integration of mmWave FMCW Radar and Stereo Camera for 3-d Automotive SAR Imaging

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IEEE TRANSACTIONS ON INSTRUMENTATION ANd MEASUREMENT 2025年 74卷

作者： Lee, Hyokbeen Kim, duk-jin Song, Juyoung An, Sangho Kim, Junwoo Seoul Natl Univ Sch Earth & Environm Sci Seoul 08826 South Korea

The millimeter wave (mmWave) synthetic aperture radar (SAR) systems are widely studied in the automotive industry. Traditional 2-d mmWave SAR systems, however, have faced challenges in accurately recognizing the shapes of objects and in providing height information. This study introduced a novel 3-d automotive mmWave SAR system that integrates a 77-GHz frequency modulation continuous wave (FMCW) radar with a stereo camera. This system processes radar signals at each point of the point cloud generated from the stereo camera to reconstruct well-focused 3-d SAR images with height information. By using an entropy-based gradient descent method for the optimized six degrees of freedom (dOF) extraction, the research achieved radar and stereo camera alignment, significantly enhancing phase estimation accuracy and target positioning. Radiometric calibrations-both relative and absolute-were, furthermore, performed, achieving relative calibration accuracy (RCA) of 0.13 dB and absolute calibration accuracy (ACA) of 0.15 dB. For the validation of our system, the 3-d SAR images of the street tree were reconstructed, demonstrating enhanced capability in accurately depicting the shapes of the complex object with backscattering coefficients. The comparative analysis demonstrated significant improvements over traditional methods, with mean square error (mse) reduced to 0.42, peak signal-to-noise ratio (PSNR) increased to 6.29, and structural similarity index (SSIM) increased to 0.35. This SAR system significantly enhances the potential for innovative automotive applications by providing accurate object information.

关键词： Radar Radar polarimetry Three-dimensional displays Radar imaging Millimeter wave communication Cameras Point cloud compression image reconstruction Radar antennas Accuracy 3-d synthetic aperture radar (SAR) image backscattering coefficient millimeter wave (mmWave) radar sensor fusion stereo camera

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deepMesh: differentiable Iso-Surface Extraction

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IEEE TRANSACTIONS ON PATTERN ANALYSIS ANd MACHINE INTELLIGENCE 2024年第11期46卷 7072-7087页

作者： Guillard, Benoit Remelli, Edoardo Lukoianov, Artem Yvernay, Pierre Richter, Stephan R. Bagautdinov, Timur Baque, Pierre Fua, Pascal Ecole Polytech Fed Lausanne Comp Vis Lab CH-1015 Lausanne Switzerland MIT Grp Geometr Data Proc Cambridge MA 02139 USA Neural Concept CH-1015 Lausanne Switzerland Intel Labs Hillsboro OR 97124 USA Meta Real Labs Menlo Pk CA 94025 USA

Geometric deep Learning has recently made striking progress with the advent of continuous deep implicit fields. They allow for detailed modeling of watertight surfaces of arbitrary topology while not relying on a 3d euclidean grid, resulting in a learnable parameterization that is unlimited in resolution. Unfortunately, these methods are often unsuitable for applications that require an explicit mesh-based surface representation because converting an implicit field to such a representation relies on the Marching Cubes algorithm, which cannot be differentiated with respect to the underlying implicit field. In this work, we remove this limitation and introduce a differentiable way to produce explicit surface mesh representations from deep Implicit Fields. Our key insight is that by reasoning on how implicit field perturbations impact local surface geometry, one can ultimately differentiate the 3d location of surface samples with respect to the underlying deep implicit field. We exploit this to define deepMesh - an end-to-end differentiable mesh representation that can vary its topology. We validate our theoretical insight through several applications: Single view 3d Reconstruction via differentiable Rendering, Physically-driven Shape Optimization, Full Scene 3d Reconstruction from Scans and End-to-End Training. In all cases our end-to-end differentiable parameterization gives us an edge over state-of-the-art algorithms.

关键词： Three-dimensional displays Topology Task analysis Surface reconstruction Shape Mesh generation Surface treatment Computational and artificial intelligence neural networks electronic design automation and methodology design methodology graphics shape computers and information processing image processing machine vision object recognition mathematics geometry computational geometry geometric modeling mathematics numerical analysis surface fitting systems engineering and theory systems, man, and cybernetics user interfaces data visualization isosurfaces

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TRRHA: A two-stream re-parameterized refocusing hybrid attention network for synthesized view quality enhancement

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displays 2024年 85卷

作者： Cao, Ziyi Li, Tiansong Wang, Guofen Yin, Haibing Wang, Hongkui Yu, Li Chongqing Normal Univ Comp & Informat Sci Chongqing 401331 Peoples R China Hangzhou Dianzi Univ Commun Engn Hangzhou 310018 Peoples R China Huazhong Univ Sci & Technol Elect Informat & Commun Wuhan 430074 Peoples R China

In multi-view video systems, the decoded texture video and its corresponding depth video are utilized to synthesize virtual views from different perspectives using the depth-image-based rendering (dIBR) technology in 3d-high efficiency video coding (3d-HEVC). However, the distortion of the compressed multi-view video and the disocclusion problem in dIBR can easily cause obvious holes and cracks in the synthesized views, degrading the visual quality of the synthesized views. To address this problem, a novel two-stream re- parameterized refocusing hybrid attention (TRRHA) network is proposed to significantly improve the quality of synthesized views. Firstly, a global multi-scale residual information stream is applied to extract the global context information by using refocusing attention module (RAM), and the RAM can detect the contextual feature and adaptively learn channel and spatial attention feature to selectively focus on different areas. Secondly, a local feature pyramid attention information stream is used to fully capture complex local texture details by using re-parameterized refocusing attention module (RRAM). The RRAM can effectively capture multi-scale texture details with different receptive fields, and adaptively adjust channel and spatial weights to adapt to information transformation at different sizes and levels. Finally, an efficient feature fusion module is proposed to effectively fuse the extracted global and local information streams. Extensive experimental results show that the proposed TRRHA achieves significantly better performance than the state-of-the-art methods. The source code will be available at https://***/647-bei/TRRHA.

关键词： 3d-HEVC Quality enhancement Synthesized view CNN Re-parameterization

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Empirical Modelling Workflow for Resolution Invariant Assessment of Osteophytes

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IEEE TRANSACTIONS ON BIOMEdICAL ENGINEERING 2024年第12期71卷 3523-3530页

作者： durongbhan, Pholpat davey, Catherine E. Stok, Kathryn S. Univ Melbourne Dept Biomed Engn Melbourne Australia Univ Melbourne Dept Biomed Engn Melbourne Vic 3052 Australia

Objective: Traditional quantitative analysis of bone microstructure in micro-computed tomography (microCT) is dependent on animal scale and requires parametric tuning in new implementations. This study aims to develop an automated and resolution-invariant 3d image processing workflow for quantitative assessment of osteophytes. Methods: In this workflow, cortical bone was segmented from microCT scans, and a 3d sphere-fitting transform was performed to obtain a thickness map, for which each voxel is assigned a thickness value corresponding to the size of the largest sphere containing the voxel that fits entirely within the cortical bone. From the thickness map, a 1-voxel thick outer surface was extracted to model surface roughness. The thickness values of the outer surface were empirically estimated by a series of known statistical distributions. Resulting parameters describing best-fit distributions, along with other cortical bone metrics, were analysed to determine sensitivity to osteoarthritis and the presence of osteophytes. Results: The workflow was validated using microCT scans and histological gradings of rabbit and rat tibiofemoral joints. Visual inspection shows that samples with osteoarthritis and the presence of osteophytes have more surface voxels assigned small thickness values. The distribution of surface thickness values for each animal is best described by Gamma distributions, whose shape parameter is consistently sensitive to osteoarthritis and the presence of osteophytes. Conclusion: Combining traditional image processing with empirical distribution fitting provides an automated, objective, and resolution-invariant workflow for osteophyte assessment. Significance: The proposed method is simple, yet elegant in its implementation, and can be readily used in new implementations.

关键词： Surface roughness Rough surfaces Bones Three-dimensional displays Rabbits Rats Cortical bone distribution fitting micro-computed tomography osteoarthritis osteophyte surface analysis

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TdPC-Net: Multi-scale lightweight and efficient 3d segmentation network with a 3d attention mechanism for brain tumor segmentation

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BIOMEdICAL SIGNAL processing ANd CONTROL 2025年 99卷

作者： Li, Yixuan Kang, Jie Shaanxi Univ Sci & Technol Sch Elect & Control Engn Xian 710021 Peoples R China

Accurate identification and segmentation of brain tumors from multimodal MRI images is essential for making treatment decisions and planning surgeries. However, the complexity associated with many neural network deployments presents challenges in meeting specific clinical accuracy requirements. In order to tackle these challenges, this paper presents a segmentation algorithm that is lightweight and efficient. Within the framework of 3d U-Net, the following enhancements are proposed: (1) in this study, we replace standard convolution with hierarchical decoupled convolution and employing trilinear interpolation for up-sampling in order to reduce model complexity;(2) in this study, based on the hierarchical decoupled convolution modules, we have also designed partially hierarchical decoupled convolution units to enhance the network's ability to capture multiscale information;(3) in this study, we introduce a 3d attention mechanism to facilitate comprehensive learning of three-dimensional information in brain tumor images, thereby enhancing the segmentation accuracy of both brain tumors and their adjacent regions. The BraTS series of datasets represents a commonly used and authoritative public resource for brain tumor image segmentation. After implementing the TdPC-Net algorithm on the BraTS 2019 validation set, we achieved dice scores of 79.07 %, 90.21 % and 84.09 % for enhancing tumor, whole tumor and tumor core, respectively. These results were attained using 0.19 million parameters and 17.96 giga floating-point operations. Our proposed model has undergone extensive testing on the BraTS 2018, BraTS 2019 and BraTS 2020 datasets, demonstrating its exceptional capability in accurately segmenting brain tumor regions. You can find the code online at: https://***/liyixuansust/TdPC-NET.

关键词： Brain tumor segmentation Lightweight Partially hierarchical decoupled convol-ution 3d attention mechanism

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Point Cloud-Based 3d Object Classification With Non Local Attention and Lightweight Convolution Neural Networks

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IEEE ACCESS 2024年 12卷 158530-158545页

作者： Karthik, R. Inamdar, Rohan Sundarr, S. Kavin Cho, Jaehyuk Veerappampalayam Easwaramoorthy, Sathishkumar Vellore Inst Technol CCPS Chennai 600127 India Vellore Inst Technol Sch Comp Sci & Engn SCOPE Chennai 600127 India Jeonbuk Natl Univ Dept Software Engn Div Elect & Informat Engn Jeonju Si 54896 Jeonrabug Do South Korea Sunway Univ Sch Engn & Technol Selangor 47500 Malaysia

Three-dimensional (3d) object classification is crucial in various applications, including autonomous driving, robotics, and augmented reality, where precise detection of objects in 3d space is required. Traditional techniques for 3d object classification encompass voxel-based representations, multi-view projections, and point cloud analysis. However, these approaches can be computationally intensive, and might not capture complete structure of the underlying object. This research introduces a novel lightweight model for 3d object classification based on point clouds that achieves higher accuracy while requiring minimal computing overhead. The proposed system incorporates adaptive sampling, an Attentive-Convolution Feature block, a dualpath feature processing block, and a non-local attention mechanism to enhance feature representation and classification performance. To our knowledge, this is the first study to propose a lightweight TnetLight module as a transformation network that aligns and converts input point clouds, capturing subtle geometric variations and highlighting differences between objects to enhance shape recognition and differentiation accuracy. Additionally, the model integrates an Attentive-Convolution Feature extraction block that combines local geometric features with global contextual information, enhancing the network's ability to capture both detailed and broader characteristics for better differentiation between objects with slight variations. The network was evaluated on the ModelNet10 dataset, achieving an accuracy of 94.4%.

关键词： Three-dimensional displays Feature extraction Shape Point cloud compression Solid modeling Object recognition Convolutional neural networks Accuracy Adaptation models Convolution 3d object classification lightweight CNN point cloud non-local attention dualpath block adaptive sampling

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A Semi-automatic Interactive Registration Software for 3d Biological images 3

A Semi-automatic Interactive Registration Software for 3D Bi...

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3rd International Conference on image processing and Media Computing (ICIPMC)

作者： Li, Yuanyuan Liu, Chao Wu, Jun Qu, Lei Huang, Haimeng Tian, Chong Yang, Bo Lai, Chuxiao AnHui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei Peoples R China China Elect Technol Grp Corp Res Inst 38 Hefei Peoples R China Southeast Univ Inst Brain & Intelligence SEU ALLEN Joint Ctr Nanjing Peoples R China

ISBN: (纸本)9798350386660;9798350386677

The complexity and variability inherent in 3d biomedical images present significant challenges for image registration. despite progress in automatic registration technologies, accurately registering three-dimensional biological images, especially those with complex structures and derived from multiple imaging modalities, still necessitates manual interaction for optimal precision. current research on interactive registration predominantly focuses on 2d slices, resulting in a loss of inter-slice constraint information for 3d biological images, and requires a lot of manual operation, resulting in low efficiency. This paper presents a pioneering semi-automatic interactive software for 3d biological image registration. Our approach innovatively integrates 3d point cloud extraction, OpenGL-based visualization, effective interaction methodologies, and non-rigid deformation techniques underpinned by both local and global constraints. A standout feature of our system is the introduction of a 3d magnetic lasso tool, which employs Gaussian constraints to significantly enhance the efficiency of interactive registration in 3d spaces. Our software not only boosts registration accuracy but also minimizes manual labor. By melding automated processes with human oversight, our solution strikes a balance between efficiency and accuracy. The validity of the work in this paper is verified by application, which provides a promising semi- automatic biological 3d image registration tool for medical research and diagnosis.

关键词： 3d biological image registration interactive software semi-automatic registration

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MR-COGraphs: Communication-Efficient Multi-Robot Open-Vocabulary Mapping System via 3d Scene Graphs

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IEEE ROBOTICS ANd AUTOMATION LETTERS 2025年第6期10卷 5713-5720页

作者： Gu, Qiuyi Ye, Zhaocheng Yu, Jincheng Tang, Jiahao Yi, Tinghao dong, Yuhan Wang, Jian Cui, Jinqiang Chen, Xinlei Wang, Yu Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China PengCheng Lab Shenzhen 518055 Guangdong Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Univ Sci & Technol China Dept Comp Sci Hefei 230002 Peoples R China Openmind Wuhu 241000 Peoples R China

Collaborative perception in unknown environments is crucial for multi-robot systems. With the emergence of foundation models, robots can now not only perceive geometric information but also achieve open-vocabulary scene understanding. However, existing map representations that support open-vocabulary queries often involve large data volumes, which becomes a bottleneck for multi-robot transmission in communication-limited environments. To address this challenge, we develop a method to construct a graph-structured 3d representation called COGraph, where nodes represent objects with semantic features and edges capture their spatial adjacency relationships. Before transmission, a data-driven feature encoder is applied to compress the feature dimensions of the COGraph. Upon receiving COGraphs from other robots, the semantic features of each node are recovered using a decoder. We also propose a feature-based approach for place recognition and translation estimation, enabling the merging of local COGraphs into a unified global map. We validate our framework on two realistic datasets and the real-world environment. The results demonstrate that, compared to existing baselines for open-vocabulary map construction, our framework reduces the data volume by over 80% while maintaining mapping and query performance without compromise.

关键词： Three-dimensional displays Semantics Robots Point cloud compression Simultaneous localization and mapping Merging image segmentation Training image edge detection Foundation models Multi-robot systems mapping semantic scene understanding 3d scene graphs

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Local Cooperative Sensing in 3d Spectrum Availability-Heterogeneous AAV Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第1期74卷 1687-1701页

作者： Li, Jiawen Wang, Yonghua Chen, Yanqing Xu, Guanghai Zheng, Bingfeng Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

The widespread application of autonomous aerial vehicles(AAVs) has put forward new demands on spectrum resources. In the more realistic spectrum availability-heterogeneous environments, where secondary users (SUs) may suffer from typically varied spectrum availability across different geographical areas, the effectiveness of the global cooperative spectrum sensing (SS) diminishes. To address this challenge, this paper investigates local cooperative sensing in 3d spectrum availability-heterogeneous cognitive radio networks (CRNs), with AAVs acting as SUs. A local cooperative sensing framework is proposed to ascertain spectrum status in this paper. Specifically, this framework utilizes the energy attenuation characteristics of the primary signal alongside a clustering algorithm to estimate primary user (PU) location, thereby facilitating the consistent spectrum availability for cooperative SUs and reducing the impacts of chaotic pathloss distribution. Then an information geometry (IG)-based data fusion method is incorporated to enable effective data processing on manifolds. Moreover, by drawing inspiration from deep learning (dL), a novel convolutional neural network (CNN) classifier based on the coordinate attention (CA) mechanism is proposed to classify the fused samples, thus achieving the spectrum decision. Numerical simulations demonstrate the superiority of the proposed scheme.

关键词： Sensors Three-dimensional displays Autonomous aerial vehicles Receivers Buildings Relays Radio spectrum management Cooperative communication Cognitive radio Urban areas Spectrum sensing AAVs spectrum availability-heterogeneity information geometry deep learning

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