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Goal-oriented Communication for Fast and Robust Robotic Fault Detection and Recovery
2026-01-26 18:32:33Shutong Chen, Adnan Aijaz, Yansha DengAbstract
Autonomous robotic systems are widely deployed in smart factories and operate in dynamic, uncertain, and human-involved environments that require low-latency and robust fault detection and recovery (FDR). However, existing FDR frameworks exhibit various limitations, such as significant delays in communication and computation, and unreliability in robot motion/trajectory generation, mainly because the communication-computation-control (3C) loop is designed without considering the downstream FDR goal. To address this, we propose a novel Goal-oriented Communication (GoC) framework that jointly designs the 3C loop tailored for fast and robust robotic FDR, with the goal of minimising the FDR time while maximising the robotic task (e.g., workpiece sorting) success rate. For fault detection, our GoC framework innovatively defines and extracts the 3D scene graph (3D-SG) as the semantic representation via our designed representation extractor, and detects faults by monitoring spatial relationship changes in the 3D-SG. For fault recovery, we fine-tune a small language model (SLM) via Low-Rank Adaptation (LoRA) and enhance its reasoning and generalization capabilities via knowledge distillation to generate recovery motions for robots. We also design a lightweight goal-oriented digital twin reconstruction module to refine the recovery motions generated by the SLM when fine-grained robotic control is required, using only task-relevant object contours for digital twin reconstruction. Extensive simulations demonstrate that our GoC framework reduces the FDR time by up to 82.6% and improves the task success rate by up to 76%, compared to the state-of-the-art frameworks that rely on vision language models for fault detection and large language models for fault recovery.
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URL
https://arxiv.org/abs/2601.18765
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FlowMorph: Physics-Consistent Self-Supervision for Label-Free Single-Cell Mechanics in Microfluidic Videos
2026-01-25 18:57:36Bora Yimenicioglu, Vishal ManikandenAbstract
Mechanical properties of red blood cells (RBCs) are promising biomarkers for hematologic and systemic disease, motivating microfluidic assays that probe deformability at throughputs of $10^3$--$10^6$ cells per experiment. However, existing pipelines rely on supervised segmentation or hand-crafted kymographs and rarely encode the laminar Stokes-flow physics that governs RBC shape evolution. We introduce FlowMorph, a physics-consistent self-supervised framework that learns a label-free scalar mechanics proxy $k$ for each tracked RBC from short brightfield microfluidic videos. FlowMorph models each cell by a low-dimensional parametric contour, advances boundary points through a differentiable ''capsule-in-flow'' combining laminar advection and curvature-regularized elastic relaxation, and optimizes a loss coupling silhouette overlap, intra-cellular flow agreement, area conservation, wall constraints, and temporal smoothness, using only automatically derived silhouettes and optical flow. Across four public RBC microfluidic datasets, FlowMorph achieves a mean silhouette IoU of $0.905$ on physics-rich videos with provided velocity fields and markedly improves area conservation and wall violations over purely data-driven baselines. On $\sim 1.5\times 10^5$ centered sequences, the scalar $k$ alone separates tank-treading from flipping dynamics with an AUC of $0.863$. Using only $200$ real-time deformability cytometry (RT-DC) events for calibration, a monotone map $E=g(k)$ predicts apparent Young's modulus with a mean absolute error of $0.118$\,MPa on $600$ held-out cells and degrades gracefully under shifts in channel geometry, optics, and frame rate.
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URL
https://arxiv.org/abs/2601.17947
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UCAD: Uncertainty-guided Contour-aware Displacement for semi-supervised medical image segmentation
2026-01-24 08:21:14Chengbo Ding, Fenghe Tang, Shaohua Kevin ZhouAbstract
Existing displacement strategies in semi-supervised segmentation only operate on rectangular regions, ignoring anatomical structures and resulting in boundary distortions and semantic inconsistency. To address these issues, we propose UCAD, an Uncertainty-Guided Contour-Aware Displacement framework for semi-supervised medical image segmentation that preserves contour-aware semantics while enhancing consistency learning. Our UCAD leverages superpixels to generate anatomically coherent regions aligned with anatomy boundaries, and an uncertainty-guided selection mechanism to selectively displace challenging regions for better consistency learning. We further propose a dynamic uncertainty-weighted consistency loss, which adaptively stabilizes training and effectively regularizes the model on unlabeled regions. Extensive experiments demonstrate that UCAD consistently outperforms state-of-the-art semi-supervised segmentation methods, achieving superior segmentation accuracy under limited annotation. The code is available at:this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2601.17366
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Filtered 2D Contour-Based Reconstruction of 3D STL Model from CT-DICOM Images
2026-01-21 13:56:43K. Punnam Chandar, Y. Ravi KumarAbstract
Reconstructing a 3D Stereo-lithography (STL) Model from 2D Contours of scanned structure in Digital Imaging and Communication in Medicine (DICOM) images is crucial to understand the geometry and deformity. Computed Tomography (CT) images are processed to enhance the contrast, reduce the noise followed by smoothing. The processed CT images are segmented using thresholding technique. 2D contour data points are extracted from segmented CT images and are used to construct 3D STL Models. The 2D contour data points may contain outliers as a result of segmentation of low resolution images and the geometry of the constructed 3D structure deviate from the actual. To cope with the imperfections in segmentation process, in this work we propose to use filtered 2D contour data points to reconstruct 3D STL Model. The filtered 2D contour points of each image are delaunay triangulated and joined layer-by-layer to reconstruct the 3D STL model. The 3D STL Model reconstruction is verified on i) 2D Data points of basic shapes and ii) Region of Interest (ROI) of human pelvic bone and are presented as case studies. The 3D STL model constructed from 2D contour data points of ROI of segmented pelvic bone with and without filtering are presented. The 3D STL model reconstructed from filtered 2D data points improved the geometry of model compared to the model reconstructed without filtering 2D data points.
Abstract (translated)
从医学数字成像和通信（DICOM）图像扫描结构的二维轮廓重建三维立体光刻（STL）模型对于理解几何形状和畸形至关重要。计算机断层扫描（CT）图像经过处理以增强对比度，减少噪声，并进行平滑处理。处理后的CT图像使用阈值技术进行分割。从分段CT图像中提取2D轮廓数据点并用于构建3D STL模型。由于低分辨率图像的分割，这些二维轮廓数据点可能包含离群点，导致构造出的三维结构与实际几何形状有所偏差。 为了应对分割过程中的缺陷，我们在此工作中提出使用过滤后的二维轮廓数据点来重建3D STL模型。每个图层上的滤波2D轮廓点进行德劳内三角剖分并层层连接以重构3D STL模型。3D STL模型的重建通过以下案例进行了验证：一是基本形状的2D数据点，二是人体髋骨感兴趣区域（ROI）。论文中展示了从分割后的髋骨ROI二维轮廓数据点构建的3D STL模型，并比较了过滤和未过滤的数据点在建模效果上的差异。结果表明，基于滤波2D数据点重建的3D STL模型比使用未经处理的数据点重构出的模型具有更好的几何形状精度。
URL
https://arxiv.org/abs/2601.14997
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Single-step Controllable Music Bandwidth Extension With Flow Matching
2026-01-20 18:26:49Carlos Hernandez-Olivan, Hendrik Vincent Koops, Hao Hao Tan, Elio QuintonAbstract
Audio restoration consists in inverting degradations of a digital audio signal to recover what would have been the pristine quality signal before the degradation occurred. This is valuable in contexts such as archives of music recordings, particularly those of precious historical value, for which a clean version may have been lost or simply does not exist. Recent work applied generative models to audio restoration, showing promising improvement over previous methods, and opening the door to the ability to perform restoration operations that were not possible before. However, making these models finely controllable remains a challenge. In this paper, we propose an extension of FLowHigh and introduce the Dynamic Spectral Contour (DSC) as a control signal for bandwidth extension via classifier-free guidance. Our experiments show competitive model performance, and indicate that DSC is a promising feature to support fine-grained conditioning.
Abstract (translated)
音频修复是指逆向处理数字音频信号中的退化，以恢复在退化发生前的原始高质量信号。这在诸如音乐录音档案的情境中尤其有价值，特别是在历史价值珍贵的情况下，可能已丢失或根本不存在干净版本的原始录音。近期的研究将生成模型应用于音频修复领域，显示出相对于先前方法的重大改进，并开启了能够执行以前无法实现的修复操作的可能性。然而，使这些模型具有精细控制性仍然是一项挑战。 在本文中，我们提出了对FLowHigh的扩展，并引入了动态频谱轮廓（Dynamic Spectral Contour, DSC）作为带宽扩展中的分类器自由引导的控制信号。我们的实验表明该方法具有竞争力的表现，并且DSC作为一个支持细粒度调节的功能显示出前景。
URL
https://arxiv.org/abs/2601.14356
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Partial Decoder Attention Network with Contour-weighted Loss Function for Data-Imbalance Medical Image Segmentation
2026-01-20 13:21:12Zhengyong Huang, Ning Jiang, Xingwen Sun, Lihua Zhang, Peng Chen, Jens Domke, Yao SuiAbstract
Image segmentation is pivotal in medical image analysis, facilitating clinical diagnosis, treatment planning, and disease evaluation. Deep learning has significantly advanced automatic segmentation methodologies by providing superior modeling capability for complex structures and fine-grained anatomical regions. However, medical images often suffer from data imbalance issues, such as large volume disparities among organs or tissues, and uneven sample distributions across different anatomical structures. This imbalance tends to bias the model toward larger organs or more frequently represented structures, while overlooking smaller or less represented structures, thereby affecting the segmentation accuracy and robustness. To address these challenges, we proposed a novel contour-weighted segmentation approach, which improves the model's capability to represent small and underrepresented structures. We developed PDANet, a lightweight and efficient segmentation network based on a partial decoder mechanism. We evaluated our method using three prominent public datasets. The experimental results show that our methodology excelled in three distinct tasks: segmenting multiple abdominal organs, brain tumors, and pelvic bone fragments with injuries. It consistently outperformed nine state-of-the-art methods. Moreover, the proposed contour-weighted strategy improved segmentation for other comparison methods across the three datasets, yielding average enhancements in Dice scores of 2.32%, 1.67%, and 3.60%, respectively. These results demonstrate that our contour-weighted segmentation method surpassed current leading approaches in both accuracy and robustness. As a model-independent strategy, it can seamlessly fit various segmentation frameworks, enhancing their performance. This flexibility highlighted its practical importance and potential for broad use in medical image analysis.
Abstract (translated)
图像分割在医学影像分析中至关重要，它有助于临床诊断、治疗计划制定以及疾病评估。深度学习通过提供对复杂结构和细微解剖区域的卓越建模能力，在自动分割方法上取得了显著进展。然而，医学图像常常受到数据不平衡问题的影响，例如不同器官或组织之间的体积差异较大，以及在不同解剖结构之间样本分布不均等。这种不平衡会导致模型偏向于更大的器官或更频繁出现的结构，而忽略较小或较少代表的结构，从而影响分割的准确性和鲁棒性。 为了解决这些问题，我们提出了一种新颖的轮廓加权分割方法，这种方法能够提升模型表示小规模和代表性较弱结构的能力。为此，我们开发了PDANet——一种基于部分解码机制的轻量级高效分割网络。我们在三个著名的公开数据集上评估了我们的方法。实验结果表明，在多器官腹部图像、脑肿瘤以及骨盆骨折碎片的分割这三项不同任务中，我们的方法均优于九种当前最先进的技术。此外，所提出的轮廓加权策略在上述所有对比方法中都带来了显著改进：分别提高了Dice分数平均值2.32%、1.67%和3.60%。这些结果表明，在准确性和鲁棒性方面，我们提出的轮廓加权分割法已经超越了当前的领先方案。 作为一种与模型无关的战略，它可以无缝地应用于各种分割框架中以提升其性能。这种灵活性突显了它在医学影像分析中的实用价值及其广泛的应用潜力。
URL
https://arxiv.org/abs/2601.14338
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Deep Image Prior with L0 Gradient Regularizer for Image Smoothing
2026-01-19 21:10:32Nhat Thanh Tran, Kevin Bui, Jack XinAbstract
Image smoothing is a fundamental image processing operation that preserves the underlying structure, such as strong edges and contours, and removes minor details and textures in an image. Many image smoothing algorithms rely on computing local window statistics or solving an optimization problem. Recent state-of-the-art methods leverage deep learning, but they require a carefully curated training dataset. Because constructing a proper training dataset for image smoothing is challenging, we propose DIP-$\ell_0$, a deep image prior framework that incorporates the $\ell_0$ gradient regularizer. This framework can perform high-quality image smoothing without any training data. To properly minimize the associated loss function that has the nonconvex, nonsmooth $\ell_0$ ``norm", we develop an alternating direction method of multipliers algorithm that utilizes an off-the-shelf $\ell_0$ gradient minimization solver. Numerical experiments demonstrate that the proposed DIP-$\ell_0$ outperforms many image smoothing algorithms in edge-preserving image smoothing and JPEG artifact removal.
Abstract (translated)
图像平滑是一种基本的图像处理操作，它保留了底层结构（如强边缘和轮廓），同时移除图像中的小细节和纹理。许多图像平滑算法依赖于计算局部窗口统计信息或求解优化问题。最近最先进的方法利用深度学习技术，但它们需要精心准备的训练数据集。由于为图像平滑构建合适的训练数据集颇具挑战性，我们提出了一种名为DIP-$\ell_0$（Deep Image Prior）的框架，并在该框架中引入了$\ell_0$梯度正则化器。这个框架能够在无需任何训练数据的情况下实现高质量的图像平滑。 为了有效最小化含有非凸且不光滑的$\ell_0$“范数”的相关损失函数，我们开发了一种交替方向乘子法算法，并利用现成的$\ell_0$梯度最小化求解器。数值实验表明，所提出的DIP-$\ell_0$框架在边缘保护型图像平滑和JPEG伪影去除方面优于许多其他图像平滑算法。
URL
https://arxiv.org/abs/2601.13400
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Diagnosis Support of Sickle Cell Anemia by Classifying Red Blood Cell Shape in Peripheral Blood Images
2026-01-19 13:15:14Wilkie Delgado-Font, Miriela Escobedo-Nicot, Manuel Gonz\'alez-Hidalgo, Silena Herold-Garcia, Antoni Jaume-i-Cap\'o, Arnau MirAbstract
Red blood cell (RBC) deformation is the consequence of several diseases, including sickle cell anemia, which causes recurring episodes of pain and severe pronounced anemia. Monitoring patients with these diseases involves the observation of peripheral blood samples under a microscope, a time-consuming procedure. Moreover, a specialist is required to perform this technique, and owing to the subjective nature of the observation of isolated RBCs, the error rate is high. In this paper, we propose an automated method for differentially enumerating RBCs that uses peripheral blood smear image analysis. In this method, the objects of interest in the image are segmented using a Chan-Vese active contour model. An analysis is then performed to classify the RBCs, also called erythrocytes, as normal or elongated or having other deformations, using the basic shape analysis descriptors: circular shape factor (CSF) and elliptical shape factor (ESF). To analyze cells that become partially occluded in a cluster during sample preparation, an elliptical adjustment is performed to allow the analysis of erythrocytes with discoidal and elongated shapes. The images of patient blood samples used in the study were acquired by a clinical laboratory specialist in the Special Hematology Department of the ``Dr. Juan Bruno Zayas'' General Hospital in Santiago de Cuba. A comparison of the results obtained by the proposed method in our experiments with those obtained by some state-of-the-art methods showed that the proposed method is superior for the diagnosis of sickle cell anemia. This superiority is achieved for evidenced by the obtained F-measure value (0.97 for normal cells and 0.95 for elongated ones) and several overall multiclass performance measures. The results achieved by the proposed method are suitable for the purpose of clinical treatment and diagnostic support of sickle cell anemia.
Abstract (translated)
URL
https://arxiv.org/abs/2601.17032
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SoundPlot: An Open-Source Framework for Birdsong Acoustic Analysis and Neural Synthesis with Interactive 3D Visualization
2026-01-19 06:17:26Naqcho Ali Mehdi, Mohammad Adeel, Aizaz Ali LarikAbstract
We present SoundPlot, an open-source framework for analyzing avian vocalizations through acoustic feature extraction, dimensionality reduction, and neural audio synthesis. The system transforms audio signals into a multi-dimensional acoustic feature space, enabling real-time visualization of temporal dynamics in 3D using web-based interactive graphics. Our framework implements a complete analysis-synthesis pipeline that extracts spectral features (centroid, bandwidth, contrast), pitch contours via probabilistic YIN (pYIN), and mel-frequency cepstral coefficients (MFCCs), mapping them to a unified timbre space for visualization. Audio reconstruction employs the Griffin-Lim phase estimation algorithm applied to mel spectrograms. The accompanying this http URL-based interface provides dual-viewport visualization comparing original and synthesized audio trajectories with independent playback controls. We demonstrate the framework's capabilities through comprehensive waveform analysis, spectrogram comparisons, and feature space evaluation using Principal Component Analysis (PCA). Quantitative evaluation shows mel spectrogram correlation scores exceeding 0.92, indicating high-fidelity preservation of perceptual acoustic structure. SoundPlot is released under the MIT License to facilitate research in bioacoustics, audio signal processing, and computational ethology.
Abstract (translated)
我们介绍了SoundPlot，这是一个开源框架，用于通过声学特征提取、降维和神经音频合成来分析鸟类鸣叫声。该系统将音频信号转换为一个多维的声学特性空间，从而能够使用基于网络的交互式图形实现实时3D时间动态可视化。我们的框架实现了一个完整的分析-综合管道，从频谱特征（中心频率、带宽、对比度）、通过概率YIN (pYIN) 提取音高轮廓以及梅尔频率倒谱系数（MFCCs），并将它们映射到一个统一的音色空间进行可视化。音频重构采用Griffin-Lim相位估计算法应用于梅尔光谱图。 该框架附带了一个基于Web的界面，提供了双视口可视化功能，可以比较原始和合成音频轨迹，并提供独立的播放控制。我们通过全面的波形分析、频谱图对比以及使用主成分分析（PCA）进行特征空间评估来展示该框架的能力。定量评价显示梅尔光谱相关得分超过0.92，表明感知声学结构的高度保真度保存。 SoundPlot根据MIT许可发布，以促进生物声学、音频信号处理和计算动物行为学的研究。
URL
https://arxiv.org/abs/2601.12752
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Unbounded Harms, Bounded Law: Liability in the Age of Borderless AI
Abstract
The rapid proliferation of artificial intelligence (AI) has exposed significant deficiencies in risk governance. While ex-ante harm identification and prevention have advanced, Responsible AI scholarship remains underdeveloped in addressing ex-post liability. Core legal questions regarding liability allocation, responsibility attribution, and remedial effectiveness remain insufficiently theorized and institutionalized, particularly for transboundary harms and risks that transcend national jurisdictions. Drawing on contemporary AI risk analyses, we argue that such harms are structurally embedded in global AI supply chains and are likely to escalate in frequency and severity due to cross-border deployment, data infrastructures, and uneven national oversight capacities. Consequently, territorially bounded liability regimes are increasingly inadequate. Using a comparative and interdisciplinary approach, this paper examines compensation and liability frameworks from high-risk transnational domains - including vaccine injury schemes, systemic financial risk governance, commercial nuclear liability, and international environmental regimes - to distill transferable legal design principles such as strict liability, risk pooling, collective risk-sharing, and liability channelling, while highlighting potential structural constraints on their application to AI-related harms. Situated within an international order shaped more by AI arms race dynamics than cooperative governance, the paper outlines the contours of a global AI accountability and compensation architecture, emphasizing the tension between geopolitical rivalry and the collective action required to govern transboundary AI risks effectively.
Abstract (translated)
人工智能（AI）的迅速普及揭示了风险管理治理方面的显著缺陷。虽然事前危害识别和预防已经有所进步，但负责任的人工智能研究在处理事后责任方面仍不够充分发展。关于责任分配、责任归属以及补救效果的核心法律问题尚未得到充分理论化和制度化，尤其是在跨国界伤害和超越国家管辖范围的风险领域。基于当代人工智能风险分析，我们认为此类损害结构性地嵌入全球AI供应链中，并且由于跨境部署、数据基础设施以及各国监管能力的不均衡，它们可能会在频率和严重程度上增加。因此，基于领土边界的责任制度变得越来越不足以应对这些挑战。 本文采用比较性和跨学科的方法，审视疫苗伤害计划、系统性金融风险治理、商业核能责任以及国际环境体制中的高风险跨国领域中补偿和责任框架，提炼出可应用于人工智能相关损害的严格责任、风险共担、集体风险管理及责任渠道转移等法律设计原则，并强调将这些原则应用到AI相关危害时可能面临的结构性限制。鉴于当前国际秩序更倾向于由AI军备竞赛动态塑造而非合作治理，在这篇论文中我们勾勒出了一个全球性的AI问责和补偿架构，突出地缘政治竞争与有效管理跨境人工智能风险所需的集体行动之间的紧张关系。
URL
https://arxiv.org/abs/2601.12646
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IMSAHLO: Integrating Multi-Scale Attention and Hybrid Loss Optimization Framework for Robust Neuronal Brain Cell Segmentation
2026-01-14 23:43:33Ujjwal Jain, Oshin Misra, Roshni Chakraborty, Mahua BhattacharyaAbstract
Accurate segmentation of neuronal cells in fluorescence microscopy is a fundamental task for quantitative analysis in computational neuroscience. However, it is significantly impeded by challenges such as the coexistence of densely packed and sparsely distributed cells, complex overlapping morphologies, and severe class imbalance. Conventional deep learning models often fail to preserve fine topological details or accurately delineate boundaries under these conditions. To address these limitations, we propose a novel deep learning framework, IMSAHLO (Integrating Multi-Scale Attention and Hybrid Loss Optimization), for robust and adaptive neuronal segmentation. The core of our model features Multi-Scale Dense Blocks (MSDBs) to capture features at various receptive fields, effectively handling variations in cell density, and a Hierarchical Attention (HA) mechanism that adaptively focuses on salient morphological features to preserve Region of Interest (ROI) boundary details. Furthermore, we introduce a novel hybrid loss function synergistically combining Tversky and Focal loss to combat class imbalance, alongside a topology-aware Centerline Dice (clDice) loss and a Contour-Weighted Boundary loss to ensure topological continuity and precise separation of adjacent cells. Large-scale experiments on the public Fluorescent Neuronal Cells (FNC) dataset demonstrate that our framework outperforms state-of-the-art architectures, achieving precision of 81.4%, macro F1 score of 82.7%, micro F1 score of 83.3%, and balanced accuracy of 99.5% on difficult dense and sparse cases. Ablation studies validate the synergistic benefits of multi-scale attention and hybrid loss terms. This work establishes a foundation for generalizable segmentation models applicable to a wide range of biomedical imaging modalities, pushing AI-assisted analysis toward high-throughput neurobiological pipelines.
Abstract (translated)
在荧光显微镜下对神经元细胞进行准确分割是计算神经科学中定量分析的一项基本任务。然而，由于密集排列和稀疏分布的细胞共存、复杂的重叠形态以及严重的类别不平衡等问题，这项工作受到了极大的阻碍。传统的深度学习模型在这种情况下往往难以保留精细的拓扑细节或精确地勾画边界。为了解决这些问题，我们提出了一种新的深度学习框架IMSAHLO（多尺度注意力与混合损失优化集成），用于稳健和适应性的神经元分割。我们的模型核心采用了多尺度密集块（MSDBs）来捕捉不同感受野的特征，有效处理细胞密度变化的问题，并且引入了层次化注意机制（HA）以自适应地聚焦于显著形态特征上，从而保留感兴趣区域（ROI）边界的细节。 此外，我们还提出了一种新的混合损失函数，该函数将Tversky和Focal loss相结合来对抗类别不平衡问题，同时结合拓扑感知的中心线Dice（clDice）损失和轮廓加权边界损失，以确保拓扑连续性和相邻细胞的精确分离。在公开的荧光神经元细胞（FNC）数据集上进行的大规模实验表明，我们的框架优于当前最先进的架构，在困难密集和稀疏案例中分别达到了81.4%的精度、82.7%的宏平均F1分数、83.3%的微平均F1分数以及99.5%的平衡准确性。消融研究验证了多尺度注意力与混合损失项之间的协同效应。 这项工作为适用于各种生物医学成像模式的一般化分割模型奠定了基础，推动了AI辅助分析向高通量神经生物学流水线方向发展。
URL
https://arxiv.org/abs/2601.11645
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LOONG: Online Time-Optimal Autonomous Flight for MAVs in Cluttered Environments
2026-01-12 11:24:54Xin Guan, Fangguo Zhao, Qianyi Wang, Chengcheng Zhao, Jiming Chen, Shuo LiAbstract
Autonomous flight of micro air vehicles (MAVs) in unknown, cluttered environments remains challenging for time-critical missions due to conservative maneuvering strategies. This article presents an integrated planning and control framework for high-speed, time-optimal autonomous flight of MAVs in cluttered environments. In each replanning cycle (100 Hz), a time-optimal trajectory under polynomial presentation is generated as a reference, with the time-allocation process accelerated by imitation learning. Subsequently, a time-optimal model predictive contouring control (MPCC) incorporates safe flight corridor (SFC) constraints at variable horizon steps to enable aggressive yet safe maneuvering, while fully exploiting the MAV's dynamics. We validate the proposed framework extensively on a custom-built LiDAR-based MAV platform. Simulation results demonstrate superior aggressiveness compared to the state of the art, while real-world experiments achieve a peak speed of 18 m/s in a cluttered environment and succeed in 10 consecutive trials from diverse start points. The video is available at the following link: this https URL.
Abstract (translated)
微型飞行器（MAV）在未知且复杂环境中的自主飞行由于保守的机动策略，对于时间关键任务来说仍然具有挑战性。本文提出了一种集成规划与控制框架，用于在复杂环境中进行高速、时间最优的MAV自主飞行。在每次重新规划周期（100 Hz）中，生成一条基于多项式的时序最优轨迹作为参考，并通过模仿学习加速了时间分配过程。随后，一个时序最优模型预测轮廓控制器（MPCC）结合可变视距步骤的安全飞行走廊（SFC）约束，实现激进且安全的机动操作，同时充分利用MAV的动力学特性。 我们在一款自定义构建的LiDAR基MAV平台上广泛验证了所提出的框架。模拟结果显示，在与现有技术相比时表现出更强的激进性，而实际实验则在复杂环境中实现了18米/秒的峰值速度，并从多个起点连续成功进行了十次试验。视频可在此链接查看：[此URL]（请将"this https URL"替换为实际提供的URL）。
URL
https://arxiv.org/abs/2601.07434
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Boosting Overlapping Organoid Instance Segmentation Using Pseudo-Label Unmixing and Synthesis-Assisted Learning
2026-01-10 17:51:09Gui Huang, Kangyuan Zheng, Xuan Cai, Jiaqi Wang, Jianjia Zhang, Kaida Ning, Wenbo Wei, Yujuan Zhu, Jiong Zhang, Mengting LiuAbstract
Organoids, sophisticated in vitro models of human tissues, are crucial for medical research due to their ability to simulate organ functions and assess drug responses accurately. Accurate organoid instance segmentation is critical for quantifying their dynamic behaviors, yet remains profoundly limited by high-quality annotated datasets and pervasive overlap in microscopy imaging. While semi-supervised learning (SSL) offers a solution to alleviate reliance on scarce labeled data, conventional SSL frameworks suffer from biases induced by noisy pseudo-labels, particularly in overlapping regions. Synthesis-assisted SSL (SA-SSL) has been proposed for mitigating training biases in semi-supervised semantic segmentation. We present the first adaptation of SA-SSL to organoid instance segmentation and reveal that SA-SSL struggles to disentangle intertwined organoids, often misrepresenting overlapping instances as a single entity. To overcome this, we propose Pseudo-Label Unmixing (PLU), which identifies erroneous pseudo-labels for overlapping instances and then regenerates organoid labels through instance decomposition. For image synthesis, we apply a contour-based approach to synthesize organoid instances efficiently, particularly for overlapping cases. Instance-level augmentations (IA) on pseudo-labels before image synthesis further enhances the effect of synthetic data (SD). Rigorous experiments on two organoid datasets demonstrate our method's effectiveness, achieving performance comparable to fully supervised models using only 10% labeled data, and state-of-the-art results. Ablation studies validate the contributions of PLU, contour-based synthesis, and augmentation-aware training. By addressing overlap at both pseudo-label and synthesis levels, our work advances scalable, label-efficient organoid analysis, unlocking new potential for high-throughput applications in precision medicine.
Abstract (translated)
类器官，即人类组织的复杂体外模型，在医学研究中至关重要，因为它们能够模拟器官功能并准确评估药物反应。精确的类器官实例分割对于量化其动态行为是至关重要的，但这一过程受到高质量标注数据集稀缺以及显微图像重叠现象的影响而受限。虽然半监督学习（SSL）可以作为缓解对稀有标签数据依赖的一种解决方案，但是传统的SSL框架因噪声伪标签所诱导的偏差问题，在重叠区域尤为明显。合成辅助的半监督学习（SA-SSL）被提出用于减轻半监督语义分割中的训练偏差。我们首次将SA-SSL应用于类器官实例分割，并揭示出SA-SSL在区分缠绕在一起的类器官时存在困难，往往将重叠实例误判为单一实体。 为了克服这个问题，我们提出了伪标签解混（PLU），这种方法能够识别重叠实例上的错误伪标签，并通过实例分解来再生类器官标签。对于图像合成，我们采用了一种基于轮廓的方法，以高效地生成类器官实例，特别是针对重叠情况特别有效。在进行图像合成之前，在伪标签上应用实例级别增强（IA）进一步增强了合成数据的效果。 我们在两个类器官数据集上的严格实验表明了该方法的有效性，使用仅10%的标注数据便能达到与全监督模型相媲美的性能，并且取得了最新的研究成果。消融研究表明PLU、基于轮廓的合成以及感知增益训练的贡献是有效的。通过在伪标签和合成水平上解决重叠问题，我们的工作推进了大规模高效标记类器官分析的发展，为精准医疗中的高通量应用解锁了新的潜力。
URL
https://arxiv.org/abs/2601.06642
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Motion Compensation for Real Time Ultrasound Scanning in Robotically Assisted Prostate Biopsy Procedures
2026-01-09 09:30:55Matija Markulin, Luka Matijevi\'c, Luka Siktar, Janko Jurdana, Branimir Caran, Marko \v{S}vaco, Filip \v{S}uligoj, Bojan \v{S}ekoranjaAbstract
Prostate cancer is one of the most common types of cancer in men. Its diagnosis by biopsy requires a high level of expertise and precision from the surgeon, so the results are highly operator-dependent. The aim of this work is to develop a robotic system for assisted ultrasound (US) examination of the prostate, a prebiopsy step that could reduce the dexterity requirements and enable faster, more accurate and more available prostate biopsy. We developed and validated a laboratory setup with a collaborative robotic arm that can autonomously scan a prostate phantom and attached the phantom to a medical robotic arm that mimics the patient's movements. The scanning robot keeps the relative position of the US probe and the prostate constant, ensuring a consistent and robust approach to reconstructing the prostate. To reconstruct the prostate, each slice is segmented to generate a series of prostate contours converted into a 3D point cloud used for biopsy planning. The average scan time of the prostate was 30 s, and the average 3D reconstruction of the prostate took 3 s. We performed four motion scenarios: the phantom was scanned in a stationary state (S), with horizontal motion (H), with vertical motion (V), and with a combination of the two (C). System validation is performed by registering the prostate point cloud reconstructions acquired during different motions (H, V, C) with those obtained in the stationary state. ICP registration with a threshold of 0.8 mm yields mean 83.2\% fitness and 0.35 mm RMSE for S-H registration, 84.1\% fitness and 0.37 mm RMSE for S-V registration and 79.4\% fitness and 0.37 mm RMSE for S-C registration. Due to the elastic and soft material properties of the prostate phantom, the maximum robot tracking error was 3 mm, which can be sufficient for prostate biopsy according to medical literature. The maximum delay in motion compensation was 0.5 s.
Abstract (translated)
前列腺癌是男性中最常见的癌症类型之一。其通过活检进行诊断，需要外科医生具备高超的技艺和精准度，因此结果很大程度上取决于操作者的技能水平。本研究旨在开发一种用于辅助超声（US）检查前列腺的机器人系统，这是一种在活检前进行的步骤，可以减少灵巧性要求，并使更快、更准确且易于获取的前列腺活检成为可能。 我们设计并验证了一个实验室设置，其中使用了一种协作式机械臂，该机械臂能够自主扫描一个模拟前列腺的模型。此外，还把这个模拟前列腺模型连接到了另一个医疗机器人手臂上，以模仿患者的运动。此系统确保了超声探针和前列腺之间的相对位置不变，从而保证了重建前列腺的一致性和稳定性。 为了重建前列腺，我们对每个切片进行了分割处理，生成了一系列前列腺轮廓，并将其转化为用于活检计划的三维点云数据。平均扫描一个前列腺的时间为30秒，而平均三维重建时间仅为3秒。我们在四种运动场景中进行了实验：模拟静止不动（S）、水平移动（H）、垂直移动（V）以及上述两种组合（C）。为了验证系统性能，我们通过不同运动状态下的点云重建结果与静止状态下获得的数据进行配准注册。 使用ICP（迭代最近点）算法并在阈值0.8毫米的情况下，S-H运动注册的平均一致性率为83.2%，均方根误差(RMSE)为0.35毫米；S-V注册的平均一致性率是84.1%，RMSE为0.37毫米；S-C组合运动注册的一致性率为79.4%，RMSE也为0.37毫米。由于模拟前列腺模型具有弹性软材料特性，机械臂的最大跟踪误差达到了3毫米，这一数值根据医学文献来说仍然足够满足进行前列腺活检的要求。同时，在运动补偿中的最大延迟为0.5秒。 这项研究展示了开发用于辅助超声检查的机器人系统的潜力，该系统能够帮助提高前列腺癌诊断的准确性和可及性，并且在实际应用中具有很高的价值。
URL
https://arxiv.org/abs/2601.05661
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Sketch&Patch++: Efficient Structure-Aware 3D Gaussian Representation
2026-01-08 21:32:54Yuang Shi, Simone Gasparini, G\'eraldine Morin, Wei Tsang OoiAbstract
We observe that Gaussians exhibit distinct roles and characteristics analogous to traditional artistic techniques -- like how artists first sketch outlines before filling in broader areas with color, some Gaussians capture high-frequency features such as edges and contours, while others represent broader, smoother regions analogous to brush strokes that add volume and depth. Based on this observation, we propose a hybrid representation that categorizes Gaussians into (i) Sketch Gaussians, which represent high-frequency, boundary-defining features, and (ii) Patch Gaussians, which cover low-frequency, smooth regions. This semantic separation naturally enables layered progressive streaming, where the compact Sketch Gaussians establish the structural skeleton before Patch Gaussians incrementally refine volumetric detail. In this work, we extend our previous method to arbitrary 3D scenes by proposing a novel hierarchical adaptive categorization framework that operates directly on the 3DGS representation. Our approach employs multi-criteria density-based clustering, combined with adaptive quality-driven refinement. This method eliminates dependency on external 3D line primitives while ensuring optimal parametric encoding effectiveness. Our comprehensive evaluation across diverse scenes, including both man-made and natural environments, demonstrates that our method achieves up to 1.74 dB improvement in PSNR, 6.7% in SSIM, and 41.4% in LPIPS at equivalent model sizes compared to uniform pruning baselines. For indoor scenes, our method can maintain visual quality with only 0.5\% of the original model size. This structure-aware representation enables efficient storage, adaptive streaming, and rendering of high-fidelity 3D content across bandwidth-constrained networks and resource-limited devices.
Abstract (translated)
我们观察到，高斯函数（Gaussians）表现出类似于传统艺术技法的角色和特征——就像艺术家在用色块填充之前会先画出轮廓一样，一些高斯函数捕捉到了高频特性如边缘和轮廓，而另一些则代表了更广泛、更平滑的区域，类似于为物体增加体积感与深度的笔触。基于这一观察，我们提出了一种混合表示方法，将高斯函数分为两类：(i) 轮廓高斯（Sketch Gaussians），用于表示高频特征和边界定义特性；(ii) 区块高斯（Patch Gaussians），则覆盖低频、平滑的区域。这种语义上的分离自然地实现了分层渐进式流媒体处理，即首先使用紧凑型轮廓高斯建立结构骨架，随后区块高斯逐步细化体积细节。 在此研究中，我们扩展了先前的方法以适应任意3D场景，并提出了一个新颖的层次自适应分类框架，该框架直接应用于三维高斯混合（3DGS）表示。我们的方法采用多标准密度聚类结合自适应质量驱动精化策略，这种方法消除了对外部3D线性原语的依赖并确保了最优参数编码效率。 我们对多样化的场景进行了全面评估，包括人造和自然环境，在相同模型大小的情况下，与均匀剪枝基线相比，我们的方法在PSNR（峰值信噪比）上提高了高达1.74 dB，在SSIM（结构相似性指数）上提高了6.7%，在LPIPS（感知损失指标）上降低了41.4%。对于室内场景，我们所提出的方法仅使用原始模型大小的0.5%就能保持视觉质量。 这种具备结构意识的表示方式能够高效地存储、适应流媒体处理以及在网络带宽有限和设备资源受限的情况下渲染高保真度的3D内容。
URL
https://arxiv.org/abs/2601.05394
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DB-MSMUNet:Dual Branch Multi-scale Mamba UNet for Pancreatic CT Scans Segmentation
2026-01-08 07:41:37Qiu Guan, Zhiqiang Yang, Dezhang Ye, Yang Chen, Xinli Xu, Ying TangAbstract
Accurate segmentation of the pancreas and its lesions in CT scans is crucial for the precise diagnosis and treatment of pancreatic cancer. However, it remains a highly challenging task due to several factors such as low tissue contrast with surrounding organs, blurry anatomical boundaries, irregular organ shapes, and the small size of lesions. To tackle these issues, we propose DB-MSMUNet (Dual-Branch Multi-scale Mamba UNet), a novel encoder-decoder architecture designed specifically for robust pancreatic segmentation. The encoder is constructed using a Multi-scale Mamba Module (MSMM), which combines deformable convolutions and multi-scale state space modeling to enhance both global context modeling and local deformation adaptation. The network employs a dual-decoder design: the edge decoder introduces an Edge Enhancement Path (EEP) to explicitly capture boundary cues and refine fuzzy contours, while the area decoder incorporates a Multi-layer Decoder (MLD) to preserve fine-grained details and accurately reconstruct small lesions by leveraging multi-scale deep semantic features. Furthermore, Auxiliary Deep Supervision (ADS) heads are added at multiple scales to both decoders, providing more accurate gradient feedback and further enhancing the discriminative capability of multi-scale features. We conduct extensive experiments on three datasets: the NIH Pancreas dataset, the MSD dataset, and a clinical pancreatic tumor dataset provided by collaborating hospitals. DB-MSMUNet achieves Dice Similarity Coefficients of 89.47%, 87.59%, and 89.02%, respectively, outperforming most existing state-of-the-art methods in terms of segmentation accuracy, edge preservation, and robustness across different datasets. These results demonstrate the effectiveness and generalizability of the proposed method for real-world pancreatic CT segmentation tasks.
Abstract (translated)
在CT扫描中准确分割胰腺及其病变对于胰腺癌的精确诊断和治疗至关重要。然而，由于多种因素（如与周围器官对比度低、解剖边界模糊、器官形状不规则以及病灶体积小等），这一任务仍具有高度挑战性。为了应对这些问题，我们提出了DB-MSMUNet（双分支多尺度眼镜蛇U型网络），这是一种专门为稳健胰腺分割而设计的新型编码器-解码器架构。 该模型的编码器采用多尺度眼镜蛇模块（MSMM）构建，结合可变形卷积和多层次状态空间建模技术以增强全局上下文建模能力和局部形变适应能力。网络采用了双解码器设计：边界解码器引入了边缘增强路径(EEP)，旨在明确捕获边界线索并细化模糊轮廓；而区域解码器则整合了多层解码器(MLD)来保留细粒度细节，并通过利用多层次深度语义特征准确重建小病灶。此外，还在两个解码器的多个尺度上增加了辅助深层监督(ADS)头，以便提供更精确的梯度反馈并进一步增强多层次特征的鉴别能力。 我们在三个数据集上进行了广泛的实验：NIH胰腺数据集、MSD数据集和由合作医院提供的临床胰腺肿瘤数据集。DB-MSMUNet在这些数据集中分别达到了89.47%、87.59% 和 89.02% 的Dice相似系数，相较于现有最先进的方法，在分割精度、边缘保持能力及跨不同数据集的鲁棒性方面均表现出色。 这些结果证明了所提出的方法在真实世界胰腺CT图像分割任务中的有效性和泛化性能。
URL
https://arxiv.org/abs/2601.04676
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Prior-Guided DETR for Ultrasound Nodule Detection
2026-01-05 15:32:58Jingjing Wang, Zhuo Xiao, Xinning Yao, Bo Liu, Lijuan Niu, Xiangzhi Bai, Fugen ZhouAbstract
Accurate detection of ultrasound nodules is essential for the early diagnosis and treatment of thyroid and breast cancers. However, this task remains challenging due to irregular nodule shapes, indistinct boundaries, substantial scale variations, and the presence of speckle noise that degrades structural visibility. To address these challenges, we propose a prior-guided DETR framework specifically designed for ultrasound nodule detection. Instead of relying on purely data-driven feature learning, the proposed framework progressively incorporates different prior knowledge at multiple stages of the network. First, a Spatially-adaptive Deformable FFN with Prior Regularization (SDFPR) is embedded into the CNN backbone to inject geometric priors into deformable sampling, stabilizing feature extraction for irregular and blurred nodules. Second, a Multi-scale Spatial-Frequency Feature Mixer (MSFFM) is designed to extract multi-scale structural priors, where spatial-domain processing emphasizes contour continuity and boundary cues, while frequency-domain modeling captures global morphology and suppresses speckle noise. Furthermore, a Dense Feature Interaction (DFI) mechanism propagates and exploits these prior-modulated features across all encoder layers, enabling the decoder to enhance query refinement under consistent geometric and structural guidance. Experiments conducted on two clinically collected thyroid ultrasound datasets (Thyroid I and Thyroid II) and two public benchmarks (TN3K and BUSI) for thyroid and breast nodules demonstrate that the proposed method achieves superior accuracy compared with 18 detection methods, particularly in detecting morphologically complex this http URL source code is publicly available at this https URL.
Abstract (translated)
准确检测超声结节对于甲状腺和乳腺癌的早期诊断和治疗至关重要。然而，由于结节形状不规则、边界模糊不清、尺度变化大以及斑点噪声导致结构可见性降低等原因，这项任务仍然极具挑战性。为了应对这些挑战，我们提出了一种基于先验知识引导的DETR框架，专门用于超声结节检测。与纯粹依赖数据驱动特征学习不同，该框架在多个网络阶段逐步引入不同的先验知识。 首先，在CNN骨干网中嵌入了具有先验正则化的空间自适应可变形前馈网络（SDFPR），以将几何先验注入到可变形采样中，从而稳定不规则和模糊结节的特征提取过程。其次，设计了一种多尺度空域频域特征混合器（MSFFM）来提取多尺度结构先验，在空间领域处理过程中突出轮廓连续性和边界线索，而在频率领域的建模则捕捉全局形态并抑制斑点噪声。 此外，密集特征交互机制（DFI）将这些受先验调制的特性在所有编码层中传播和利用，使解码器能够在一致的几何结构指导下提升查询精炼。我们在两个临床收集的甲状腺超声数据集（Thyroid I 和 Thyroid II）以及两个公开基准测试（TN3K 和 BUSI）上进行了实验，这些实验针对的是甲状腺和乳腺结节的检测问题。结果表明，所提出的方法在18种检测方法中表现出了卓越的准确性，特别是在形态复杂度高的情况下。 该方法的源代码可在以下链接处获取：[具体URL应由原文提供者给出]
URL
https://arxiv.org/abs/2601.02212
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Boosting Segment Anything Model to Generalize Visually Non-Salient Scenarios
2026-01-02 02:42:04Guangqian Guo, Pengfei Chen, Yong Guo, Huafeng Chen, Boqiang Zhang, Shan GaoAbstract
Segment Anything Model (SAM), known for its remarkable zero-shot segmentation capabilities, has garnered significant attention in the community. Nevertheless, its performance is challenged when dealing with what we refer to as visually non-salient scenarios, where there is low contrast between the foreground and background. In these cases, existing methods often cannot capture accurate contours and fail to produce promising segmentation results. In this paper, we propose Visually Non-Salient SAM (VNS-SAM), aiming to enhance SAM's perception of visually non-salient scenarios while preserving its original zero-shot generalizability. We achieve this by effectively exploiting SAM's low-level features through two designs: Mask-Edge Token Interactive decoder and Non-Salient Feature Mining module. These designs help the SAM decoder gain a deeper understanding of non-salient characteristics with only marginal parameter increments and computational requirements. The additional parameters of VNS-SAM can be optimized within 4 hours, demonstrating its feasibility and practicality. In terms of data, we established VNS-SEG, a unified dataset for various VNS scenarios, with more than 35K images, in contrast to previous single-task adaptations. It is designed to make the model learn more robust VNS features and comprehensively benchmark the model's segmentation performance and generalizability on VNS scenarios. Extensive experiments across various VNS segmentation tasks demonstrate the superior performance of VNS-SAM, particularly under zero-shot settings, highlighting its potential for broad real-world applications. Codes and datasets are publicly available at this https URL.
Abstract (translated)
段落翻译如下： Segment Anything Model (SAM)，因其卓越的零样本分割能力而闻名，已经在社区中引起了广泛关注。然而，在我们称之为视觉非显著场景（即前景和背景对比度较低）的情况下，其性能受到挑战。在这些情况下，现有的方法往往无法准确捕捉轮廓，并且难以生成有希望的分割结果。在本文中，我们提出了Visually Non-Salient SAM (VNS-SAM)，旨在增强SAM对视觉非显著场景的理解能力，同时保持其原有的零样本泛化能力。通过有效利用SAM的低级特征，我们在Mask-Edge Token Interactive解码器和Non-Salient Feature Mining模块的设计上实现了这一点。这些设计帮助SAM解码器在参数增量和计算需求仅略有增加的情况下更深入地理解非显著特性。VNS-SAM中的额外参数可以在4小时内优化完成，这证明了其可行性和实用性。 在数据方面，我们建立了VNS-SEG，这是一个统一的、针对各种视觉非显著场景的数据集，包含超过35,000张图像，与之前的单一任务适应相比具有明显优势。该数据集旨在使模型学习更稳健的视觉非显著特性，并全面评估模型在视觉非显著场景中的分割性能和泛化能力。通过多种视觉非显著分割任务进行广泛的实验表明，在零样本设置下VNS-SAM表现出色，突显了其在广泛现实世界应用中的潜力。代码和数据集公开可用，请访问此链接：[提供链接]。 请注意，原文的最后提供了代码和数据集的访问链接，您需要根据实际情况填写具体URL地址以供读者参考。
URL
https://arxiv.org/abs/2601.00537
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3D Path-Following Guidance via Nonlinear Model Predictive Control for Fixed-Wing Small UAS
Abstract
This paper presents the design, implementation, and flight test results of two novel 3D path-following guidance algorithms based on nonlinear model predictive control (MPC), with specific application to fixed-wing small uncrewed aircraft systems. To enable MPC, control-augmented modelling and system identification of the RAAVEN small uncrewed aircraft is presented. Two formulations of MPC are then showcased. The first schedules a static reference path rate over the MPC horizon, incentivizing a constant inertial speed. The second, with inspiration from model predictive contouring control, dynamically optimizes for the reference path rate over the controller horizon as the system operates. This allows for a weighted tradeoff between path progression and distance from path, two competing objectives in path-following guidance. Both controllers are formulated to operate over general smooth 3D arc-length parameterized curves. The MPC guidance algorithms are flown over several high-curvature test paths, with comparison to a baseline lookahead guidance law. The results showcase the real-world feasibility and superior performance of nonlinear MPC for 3D path-following guidance at ground speeds up to 36 meters per second.
Abstract (translated)
本文介绍了两种基于非线性模型预测控制（MPC）的新型三维路径跟踪引导算法的设计、实现及飞行测试结果，这些算法专门应用于固定翼小型无人驾驶飞机系统。为了启用MPC，文中展示了对RAAVEN小型无人驾驶飞机的增控建模和系统识别过程。然后，介绍了两种MPC的构型。 第一种方法在整个模型预测控制时域内安排静态参考路径速率，旨在激励恒定惯性速度。第二种方法受到模型预测轮廓控制的启发，在控制器操作期间动态优化参考路径速率。这使得在路径跟踪引导中两个竞争目标——路径进展和远离路径的距离之间可以进行加权折衷。 这两种控制器都设计为可以在通用平滑三维弧长参数化曲线范围内运行。MPC引导算法已在多个高曲率测试路径上进行了飞行，与基线前瞻式导引法则进行了比较。结果表明，在高达每秒36米的速度下，非线性MPC在三维路径跟踪中的实际可行性及其优越性能。
URL
https://arxiv.org/abs/2512.24326
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Contour Information Aware 2D Gaussian Splatting for Image Representation
2025-12-29 07:24:36Masaya Takabe, Hiroshi Watanabe, Sujun Hong, Tomohiro Ikai, Zheming Fan, Ryo Ishimoto, Kakeru Sugimoto, Ruri ImichiAbstract
Image representation is a fundamental task in computer vision. Recently, Gaussian Splatting has emerged as an efficient representation framework, and its extension to 2D image representation enables lightweight, yet expressive modeling of visual content. While recent 2D Gaussian Splatting (2DGS) approaches provide compact storage and real-time decoding, they often produce blurry or indistinct boundaries when the number of Gaussians is small due to the lack of contour awareness. In this work, we propose a Contour Information-Aware 2D Gaussian Splatting framework that incorporates object segmentation priors into Gaussian-based image representation. By constraining each Gaussian to a specific segmentation region during rasterization, our method prevents cross-boundary blending and preserves edge structures under high compression. We also introduce a warm-up scheme to stabilize training and improve convergence. Experiments on synthetic color charts and the DAVIS dataset demonstrate that our approach achieves higher reconstruction quality around object edges compared to existing 2DGS methods. The improvement is particularly evident in scenarios with very few Gaussians, while our method still maintains fast rendering and low memory usage.
Abstract (translated)
图像表示是计算机视觉中的一个基础任务。最近，高斯点绘（Gaussian Splatting）作为一种高效的表示框架崭露头角，并且其扩展到2D图像表示中使轻量级、但表达能力强的视觉内容建模成为可能。尽管最近提出的二维高斯点绘（2DGS）方法提供了紧凑的存储和实时解码，但在高斯数量较少时，由于缺乏轮廓感知，它们常常会产生模糊或边界不清晰的效果。 在本文工作中，我们提出了一种基于轮廓信息的二维高斯点绘框架，该框架将物体分割先验知识融入到基于高斯分布的图像表示中。通过限制每个高斯分布在栅格化过程中的特定分割区域内，我们的方法可以防止跨边界的混合，并在高度压缩的情况下保留边缘结构。我们还引入了一种预热方案来稳定训练并提高收敛速度。 实验结果表明，在合成颜色图表和DAVIS数据集上，相较于现有的2DGS方法，我们的方法在物体边缘的重建质量方面表现更优。尤其是在高斯数量非常少的情景下，改进尤为显著，而同时我们的方法仍保持快速渲染和低内存使用的特点。
URL
https://arxiv.org/abs/2512.23255
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