Contour
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RotaTouille: Rotation Equivariant Deep Learning for Contours
2025-08-22 13:05:55Odin Hoff Gardaa, Nello BlaserAbstract
Contours or closed planar curves are common in many domains. For example, they appear as object boundaries in computer vision, isolines in meteorology, and the orbits of rotating machinery. In many cases when learning from contour data, planar rotations of the input will result in correspondingly rotated outputs. It is therefore desirable that deep learning models be rotationally equivariant. In addition, contours are typically represented as an ordered sequence of edge points, where the choice of starting point is arbitrary. It is therefore also desirable for deep learning methods to be equivariant under cyclic shifts. We present RotaTouille, a deep learning framework for learning from contour data that achieves both rotation and cyclic shift equivariance through complex-valued circular convolution. We further introduce and characterize equivariant non-linearities, coarsening layers, and global pooling layers to obtain invariant representations for downstream tasks. Finally, we demonstrate the effectiveness of RotaTouille through experiments in shape classification, reconstruction, and contour regression.
Abstract (translated)
轮廓或封闭的平面曲线在许多领域都很常见。例如，在计算机视觉中，它们作为物体边界出现；在气象学中，它们表现为等值线；在旋转机械中，则是其轨道。在很多情况下，从轮廓数据学习时，输入的平面旋转会导致相应的输出旋转。因此，希望深度学习模型具有旋转等变性（即对旋转保持不变的关系）。此外，轮廓通常被表示为一系列边缘点的有序序列，并且起点的选择是任意的。因此，也希望深度学习方法在循环位移下保持等变性。 我们提出了RotaTouille，这是一种基于复杂值圆卷积实现同时具有旋转和循环位移等变性的深度学习框架，以从轮廓数据中进行学习。此外，我们引入并描述了等变非线性、细化层以及全局池化层，以便为下游任务获取不变表示。最后，通过形状分类、重构及轮廓回归的实验，展示了RotaTouille的有效性。
URL
https://arxiv.org/abs/2508.16359
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Towards PerSense++: Advancing Training-Free Personalized Instance Segmentation in Dense Images
2025-08-20 12:23:08Muhammad Ibraheem Siddiqui, Muhammad Umer Sheikh, Hassan Abid, Kevin Henry, Muhammad Haris KhanAbstract
Segmentation in dense visual scenes poses significant challenges due to occlusions, background clutter, and scale variations. To address this, we introduce PerSense, an end-to-end, training-free, and model-agnostic one-shot framework for Personalized instance Segmentation in dense images. PerSense employs a novel Instance Detection Module (IDM) that leverages density maps (DMs) to generate instance-level candidate point prompts, followed by a Point Prompt Selection Module (PPSM) that filters false positives via adaptive thresholding and spatial gating. A feedback mechanism further enhances segmentation by automatically selecting effective exemplars to improve DM quality. We additionally present PerSense++, an enhanced variant that incorporates three additional components to improve robustness in cluttered scenes: (i) a diversity-aware exemplar selection strategy that leverages feature and scale diversity for better DM generation; (ii) a hybrid IDM combining contour and peak-based prompt generation for improved instance separation within complex density patterns; and (iii) an Irrelevant Mask Rejection Module (IMRM) that discards spatially inconsistent masks using outlier analysis. Finally, to support this underexplored task, we introduce PerSense-D, a dedicated benchmark for personalized segmentation in dense images. Extensive experiments across multiple benchmarks demonstrate that PerSense++ outperforms existing methods in dense settings.
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URL
https://arxiv.org/abs/2508.14660
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WiseLVAM: A Novel Framework For Left Ventricle Automatic Measurements
2025-08-16 11:58:01Durgesh Kumar Singh, Qing Cao, Sarina Thomas, Ahc\`ene Boubekki, Robert Jenssen, Michael KampffmeyerAbstract
Clinical guidelines recommend performing left ventricular (LV) linear measurements in B-mode echocardiographic images at the basal level -- typically at the mitral valve leaflet tips -- and aligned perpendicular to the LV long axis along a virtual scanline (SL). However, most automated methods estimate landmarks directly from B-mode images for the measurement task, where even small shifts in predicted points along the LV walls can lead to significant measurement errors, reducing their clinical reliability. A recent semi-automatic method, EnLVAM, addresses this limitation by constraining landmark prediction to a clinician-defined SL and training on generated Anatomical Motion Mode (AMM) images to predict LV landmarks along the same. To enable full automation, a contour-aware SL placement approach is proposed in this work, in which the LV contour is estimated using a weakly supervised B-mode landmark detector. SL placement is then performed by inferring the LV long axis and the basal level-mimicking clinical guidelines. Building on this foundation, we introduce \textit{WiseLVAM} -- a novel, fully automated yet manually adaptable framework for automatically placing the SL and then automatically performing the LV linear measurements in the AMM mode. \textit{WiseLVAM} utilizes the structure-awareness from B-mode images and the motion-awareness from AMM mode to enhance robustness and accuracy with the potential to provide a practical solution for the routine clinical application.
Abstract (translated)
临床指南推荐在B型超声心动图图像的基础水平（通常是在二尖瓣叶尖）进行左心室（LV）线性测量，并且这些测量应该沿着与LV长轴垂直的虚拟扫描线（SL）进行。然而，大多数自动化方法直接从B模式图像估计地标点以完成测量任务，即使预测点在LV壁上的微小偏移也可能导致显著的测量误差，从而降低其临床可靠性。 最近的一种半自动方法EnLVAM通过限制地标预测在由临床医生定义的扫描线上，并使用生成的解剖运动模式（AMM）图像进行训练来解决这一问题。这种方法可以预测沿着相同路径的心室地标位置。为了实现完全自动化，在本研究中提出了一种基于轮廓感知的SL放置方法，其中LV轮廓通过弱监督B模式地标检测器进行估计。然后根据推断出的LV长轴和模仿临床指南中的基础水平来进行扫描线（SL）定位。 在此基础上，我们介绍了\textit{WiseLVAM}——一种全新的、完全自动化的但又可手动调整的框架，用于在AMM模式下自动放置扫描线并进行LV线性测量。该方法利用B模式图像中的结构信息和AMM模式中的运动信息来增强其鲁棒性和准确性，并具有提供实用解决方案以供常规临床应用的潜力。
URL
https://arxiv.org/abs/2508.12023
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Statistical analysis of multivariate planar curves and applications to X-ray classification
2025-08-15 19:13:27Moindji\'e Issam-Ali, Descary Marie-H\'el\`ene, Beaulac C\'edricAbstract
Recent developments in computer vision have enabled the availability of segmented images across various domains, such as medicine, where segmented radiography images play an important role in diagnosis-making. As prediction problems are common in medical image analysis, this work explores the use of segmented images (through the associated contours they highlight) as predictors in a supervised classification context. Consequently, we develop a new approach for image analysis that takes into account the shape of objects within images. For this aim, we introduce a new formalism that extends the study of single random planar curves to the joint analysis of multiple planar curves-referred to here as multivariate planar curves. In this framework, we propose a solution to the alignment issue in statistical shape analysis. The obtained multivariate shape variables are then used in functional classification methods through tangent projections. Detection of cardiomegaly in segmented X-rays and numerical experiments on synthetic data demonstrate the appeal and robustness of the proposed method.
Abstract (translated)
近期在计算机视觉领域的进展使得各种领域（如医学）中的分割图像变得可用，其中放射影像的分割图在诊断中发挥重要作用。鉴于预测问题是医学影像分析中的常见问题，本研究探索了将分割图像（通过它们所强调的轮廓）作为监督分类上下文中预测因子的应用。因此，我们开发了一种新的图像分析方法，该方法考虑到了图像内对象的形状。为了实现这一目标，我们引入了一种新形式化的方法，它扩展了对单一随机平面曲线的研究到多个平面曲线（在本文中称为多元平面曲线）的同时分析。在此框架下，我们提出了解决统计形状分析中的对齐问题的一种方案。所获得的多元形状变量随后被用于通过切线投影应用于功能性分类方法。心脏病理性心脏扩大在分割X光片上的检测以及合成数据的数值实验展示了所提议的方法的魅力和鲁棒性。
URL
https://arxiv.org/abs/2508.11780
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Nominal Evaluation Of Automatic Multi-Sections Control Potential In Comparison To A Simpler One- Or Two-Sections Alternative With Predictive Spray Switching
Abstract
Automatic Section Control (ASC) is a long-standing trend for spraying in agriculture. It promises to minimise spray overlap areas. The core idea is to (i) switch off spray nozzles on areas that have already been sprayed, and (ii) to dynamically adjust nozzle flow rates along the boom bar that holds the spray nozzles when velocities of boom sections vary during turn maneuvers. ASC is not possible without sensors, in particular for accurate positioning data. Spraying and the movement of modern wide boom bars are highly dynamic processes. In addition, many uncertainty factors have an effect such as cross wind drift, boom height, nozzle clogging in open-field conditions, and so forth. In view of this complexity, the natural question arises if a simpler alternative exist. Therefore, an Automatic Multi-Sections Control method is compared to a proposed simpler one- or two-sections alternative that uses predictive spray switching. The comparison is provided under nominal conditions. Agricultural spraying is intrinsically linked to area coverage path planning and spray switching logic. Combinations of two area coverage path planning and switching logics as well as three sections-setups are compared. The three sections-setups differ by controlling 48 sections, 2 sections or controlling all nozzles uniformly with the same control signal as one single section. Methods are evaluated on 10 diverse real-world field examples, including non-convex field contours, freeform mainfield lanes and multiple obstacle areas. A preferred method is suggested that (i) minimises area coverage pathlength, (ii) offers intermediate overlap, (iii) is suitable for manual driving by following a pre-planned predictive spray switching logic for an area coverage path plan, and (iv) and in contrast to ASC can be implemented sensor-free and therefore at low cost.
Abstract (translated)
自动分区控制（ASC）在农业喷洒中是一个长期的趋势，它承诺能够最小化喷洒重叠区域。核心理念是：(i) 关闭已经完成喷洒的区域上的喷嘴，并(ii) 在转向操作过程中动态调整喷杆上各喷嘴流速以应对不同部分速度的变化。没有传感器，特别是精准定位数据的情况下，ASC是不可能实现的。现代农业中宽幅喷杆的喷洒过程和移动是高度动态化的，此外还有许多不确定性因素会影响其效果，比如侧风漂移、喷杆高度、开放环境下的喷头堵塞等问题。鉴于这种复杂性，一个自然的问题便产生了：是否有一种更简单的替代方法存在。 因此，自动多分区控制法与一种使用预测式喷洒切换的单区或双区简化方案进行了比较，其中比较是在标准条件下进行的。农业喷洒本质上与覆盖路径规划和喷洒切换逻辑密切相关。两种面积覆盖路径规划及三种不同分区设置（分别控制48个部分、2个部分或者所有喷嘴使用相同的单一信号统一控制）的组合被加以对比。 这种方法在10种不同的实际田地情况中进行了评估，包括非凸轮廓、自由形状的主要农田道路以及多个障碍区域。推荐的方法是：(i) 最小化覆盖路径长度；(ii) 提供适度重叠；(iii) 适合手动驾驶，并遵循预规划的预测式喷洒切换逻辑进行覆盖路径计划；(iv) 并且与ASC不同，这种方法可以无传感器实现，因此成本较低。
URL
https://arxiv.org/abs/2508.11573
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Towards Fully Onboard State Estimation and Trajectory Tracking for UAVs with Suspended Payloads
2025-08-15 15:48:42Martin Jirou\v{s}ek, Tom\'a\v{s} B\'a\v{c}a, Martin SaskaAbstract
This paper addresses the problem of tracking the position of a cable-suspended payload carried by an unmanned aerial vehicle, with a focus on real-world deployment and minimal hardware requirements. In contrast to many existing approaches that rely on motion-capture systems, additional onboard cameras, or instrumented payloads, we propose a framework that uses only standard onboard sensors--specifically, real-time kinematic global navigation satellite system measurements and data from the onboard inertial measurement unit--to estimate and control the payload's position. The system models the full coupled dynamics of the aerial vehicle and payload, and integrates a linear Kalman filter for state estimation, a model predictive contouring control planner, and an incremental model predictive controller. The control architecture is designed to remain effective despite sensing limitations and estimation uncertainty. Extensive simulations demonstrate that the proposed system achieves performance comparable to control based on ground-truth measurements, with only minor degradation (< 6%). The system also shows strong robustness to variations in payload parameters. Field experiments further validate the framework, confirming its practical applicability and reliable performance in outdoor environments using only off-the-shelf aerial vehicle hardware.
Abstract (translated)
本文研究了由无人驾驶航空器携带的悬挂在缆索上的负载的位置跟踪问题，特别关注实际部署和最小硬件需求。与许多现有的依赖运动捕捉系统、额外机载摄像机或仪器化负载的方法不同，我们提出了一种仅使用标准机载传感器——实时动态全球导航卫星系统测量和机载惯性测量单元数据——来估计和控制负载位置的框架。该系统模拟了空中飞行器和负载之间的完整耦合动力学，并整合了一个线性卡尔曼滤波器用于状态估计、模型预测轮廓控制器规划器以及增量模型预测控制器。该控制系统架构即使在传感限制和估计不确定性的情况下也能保持有效性。广泛的仿真结果表明，所提出的系统实现了与基于地面真实测量控制相媲美的性能，仅略有下降（<6%）。此外，该系统还显示出对负载参数变化的强鲁棒性。现场实验进一步验证了该框架的有效性，在仅使用现成的飞行器硬件的情况下确认其在户外环境中的实际应用性和可靠性能。
URL
https://arxiv.org/abs/2508.11547
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Reactive Model Predictive Contouring Control for Robot Manipulators
2025-08-13 05:22:00Junheon Yoon, Woo-Jeong Baek, Jaeheung ParkAbstract
This contribution presents a robot path-following framework via Reactive Model Predictive Contouring Control (RMPCC) that successfully avoids obstacles, singularities and self-collisions in dynamic environments at 100 Hz. Many path-following methods rely on the time parametrization, but struggle to handle collision and singularity avoidance while adhering kinematic limits or other constraints. Specifically, the error between the desired path and the actual position can become large when executing evasive maneuvers. Thus, this paper derives a method that parametrizes the reference path by a path parameter and performs the optimization via RMPCC. In particular, Control Barrier Functions (CBFs) are introduced to avoid collisions and singularities in dynamic environments. A Jacobian-based linearization and Gauss-Newton Hessian approximation enable solving the nonlinear RMPCC problem at 100 Hz, outperforming state-of-the-art methods by a factor of 10. Experiments confirm that the framework handles dynamic obstacles in real-world settings with low contouring error and low robot acceleration.
Abstract (translated)
这篇贡献介绍了一种基于反应式模型预测轮廓控制（Reactive Model Predictive Contouring Control，RMPCC）的机器人路径跟踪框架，能够在动态环境中以100 Hz的速度成功地避开障碍物、奇点和自我碰撞。许多路径跟踪方法依赖于时间参数化，但难以在遵守运动学限制或其他约束条件的同时处理碰撞及奇异点避免问题。特别地，在执行避让操作时，期望路径与实际位置之间的误差可能会变得非常大。因此，本文提出了一种通过路径参数对参考路径进行参数化的办法，并采用RMPCC进行优化的方法。具体来说，引入了控制障碍函数（Control Barrier Functions，CBFs）以避免动态环境中的碰撞和奇异点问题。基于雅可比矩阵的线性化以及高斯-牛顿海森矩阵近似方法使得非线性的RMPCC问题能够在100 Hz的速度下解决，并且其性能优于现有技术水平的方法十倍以上。实验结果证实，该框架在现实世界的应用中能够处理动态障碍物，具有低轮廓误差和低机器人加速度的特点。
URL
https://arxiv.org/abs/2508.09502
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Skyshield: Event-Driven Submillimetre Thin Obstacle Detection for Drone Flight Safety
2025-08-13 00:26:35Zhengli Zhang, Xinyu Luo, Yuchen Sun, Wenhua Ding, Dongyu Huang, Xinlei ChenAbstract
Drones operating in complex environments face a significant threat from thin obstacles, such as steel wires and kite strings at the submillimeter level, which are notoriously difficult for conventional sensors like RGB cameras, LiDAR, and depth cameras to detect. This paper introduces SkyShield, an event-driven, end-to-end framework designed for the perception of submillimeter scale obstacles. Drawing upon the unique features that thin obstacles present in the event stream, our method employs a lightweight U-Net architecture and an innovative Dice-Contour Regularization Loss to ensure precise detection. Experimental results demonstrate that our event-based approach achieves mean F1 Score of 0.7088 with a low latency of 21.2 ms, making it ideal for deployment on edge and mobile platforms.
Abstract (translated)
在复杂环境中运行的无人机面临着来自细小障碍物（如钢丝和风筝线）的重大威胁，这些障碍物通常小于毫米级，对于传统的传感器来说非常难以检测，比如RGB相机、激光雷达和深度摄像头。本文介绍了SkyShield，这是一种基于事件驱动、端到端设计的框架，专门用于感知毫米以下级别的障碍物。通过利用细小障碍物在事件流中呈现的独特特征，我们的方法采用了轻量级U-Net架构，并创新性地引入了Dice轮廓正则化损失，以确保精确检测。 实验结果显示，基于事件的方法实现了0.7088的平均F1分数和21.2毫秒的低延迟，使其非常适合部署在边缘计算和移动平台上。
URL
https://arxiv.org/abs/2508.09397
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Real-Time Intuitive AI Drawing System for Collaboration: Enhancing Human Creativity through Formal and Contextual Intent Integration
2025-08-12 01:34:23Jookyung Song, Mookyoung Kang, Nojun KwakAbstract
This paper presents a real-time generative drawing system that interprets and integrates both formal intent - the structural, compositional, and stylistic attributes of a sketch - and contextual intent - the semantic and thematic meaning inferred from its visual content - into a unified transformation process. Unlike conventional text-prompt-based generative systems, which primarily capture high-level contextual descriptions, our approach simultaneously analyzes ground-level intuitive geometric features such as line trajectories, proportions, and spatial arrangement, and high-level semantic cues extracted via vision-language models. These dual intent signals are jointly conditioned in a multi-stage generation pipeline that combines contour-preserving structural control with style- and content-aware image synthesis. Implemented with a touchscreen-based interface and distributed inference architecture, the system achieves low-latency, two-stage transformation while supporting multi-user collaboration on shared canvases. The resulting platform enables participants, regardless of artistic expertise, to engage in synchronous, co-authored visual creation, redefining human-AI interaction as a process of co-creation and mutual enhancement.
Abstract (translated)
本文介绍了一种实时生成绘画系统，该系统能够解读并整合两种意图：形式意图——草图的结构、构图和风格属性；以及情境意图——从视觉内容中推断出的语义和主题含义，并将它们统一到一个转换过程中。与传统的基于文本提示的生成系统主要捕捉高层次的情境描述不同，我们的方法同时分析了基础层面直观的几何特征（如线条轨迹、比例及空间布局）和通过视觉语言模型提取的高层次语义线索。这些双重意图信号在一个多阶段生成管道中联合调节，该管道结合了保持轮廓结构控制与风格和内容感知图像合成的方法。系统采用触摸屏界面和分布式推理架构实现，在支持多人协作的同时实现了低延迟、两阶段转换，并且参与者无论是否有艺术背景都可以进行同步的共同创作，重新定义了人机交互作为一种共创和相互增强的过程。
URL
https://arxiv.org/abs/2508.19254
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BEVANet: Bilateral Efficient Visual Attention Network for Real-Time Semantic Segmentation
2025-08-10 11:24:05Ping-Mao Huang, I-Tien Chao, Ping-Chia Huang, Jia-Wei Liao, Yung-Yu ChuangAbstract
Real-time semantic segmentation presents the dual challenge of designing efficient architectures that capture large receptive fields for semantic understanding while also refining detailed contours. Vision transformers model long-range dependencies effectively but incur high computational cost. To address these challenges, we introduce the Large Kernel Attention (LKA) mechanism. Our proposed Bilateral Efficient Visual Attention Network (BEVANet) expands the receptive field to capture contextual information and extracts visual and structural features using Sparse Decomposed Large Separable Kernel Attentions (SDLSKA). The Comprehensive Kernel Selection (CKS) mechanism dynamically adapts the receptive field to further enhance performance. Furthermore, the Deep Large Kernel Pyramid Pooling Module (DLKPPM) enriches contextual features by synergistically combining dilated convolutions and large kernel attention. The bilateral architecture facilitates frequent branch communication, and the Boundary Guided Adaptive Fusion (BGAF) module enhances boundary delineation by integrating spatial and semantic features under boundary guidance. BEVANet achieves real-time segmentation at 33 FPS, yielding 79.3% mIoU without pretraining and 81.0% mIoU on Cityscapes after ImageNet pretraining, demonstrating state-of-the-art performance. The code and model is available at this https URL.
Abstract (translated)
实时语义分割面临着既要设计高效架构来捕获大感受野以进行语义理解，又要细化详细轮廓的双重挑战。视觉变换器能够有效地建模长距离依赖关系，但会带来较高的计算成本。为了解决这些问题，我们引入了大型内核注意力（LKA）机制。我们的提出的双边高效视觉注意网络（BEVANet）扩展感受野以捕获上下文信息，并使用稀疏分解的大可分离内核注意（SDLSKA）提取视觉和结构特征。综合核选择（CKS）机制动态调整感受野，进一步提升性能。此外，深度大型内核金字塔池化模块（DLKPPM）通过结合膨胀卷积和大型内核注意力来丰富上下文特征。双边架构促进了频繁的分支通信，并且边界引导自适应融合（BGAF）模块在边界指引下整合空间和语义特征以增强轮廓定义。 BEVANet实现了每秒33帧的速度，在不进行预训练的情况下，mIoU达到了79.3%，而在Cityscapes数据集上经过ImageNet预训练后，mIoU达到81.0%。这显示了它在实时语义分割任务中的卓越性能。代码和模型可以在提供的链接中获取。
URL
https://arxiv.org/abs/2508.07300
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The 2D+ Dynamic Articulatory Model DYNARTmo: Tongue-Palate Contact Area Estimation
Abstract
This paper describes an extension of the two-dimensional dynamic articulatory model DYNARTmo by integrating an internal three-dimensional representation of the palatal dome to estimate tongue-palate contact areas from midsagittal tongue contours. Two alternative dome geometries - a half-ellipse and a cosine based profile - are implemented to model lateral curvature in the coronal plane. Using these geometries, lateral contact points are analytically computed for each anterior-posterior position, enabling the generation of electropalatography-like visualizations within the 2D+ framework. The enhanced model supports three synchronized views (sagittal, glottal, and palatal) for static and dynamic (animated) articulation displays, suitable for speech science education and speech therapy. Future work includes adding a facial (lip) view and implementing articulatory-to-acoustic synthesis to quantitatively evaluate model realism.
Abstract (translated)
本文描述了对二维动态发音模型DYNARTmo的扩展，通过整合硬腭穹顶的内部三维表示来估算从舌中切面轮廓到软腭接触区域的位置。文中实现了两种替代性的穹顶几何形状——半椭圆和基于余弦曲线的轮廓——以在冠状平面上模拟侧向曲率。利用这些几何结构，对于每个前后位置，可以计算出侧向接触点，并在2D+框架内生成类似于电感式腭图（electropalatography）的可视化效果。增强后的模型支持三种同步视图（舌中切面、声门和软腭），适用于静态和动态（动画）发音展示，在言语科学研究与言语治疗方面具有应用价值。未来的工作将包括添加面部（唇部）视图以及实现发音到声学合成，以定量评估模型的真实度。
URL
https://arxiv.org/abs/2508.07262
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Edge Detection for Organ Boundaries via Top Down Refinement and SubPixel Upsampling
2025-08-09 03:28:12Aarav Mehta, Priya Deshmukh, Vikram Singh, Siddharth Malhotra, Krishnan Menon Iyer, Tanvi IyerAbstract
Accurate localization of organ boundaries is critical in medical imaging for segmentation, registration, surgical planning, and radiotherapy. While deep convolutional networks (ConvNets) have advanced general-purpose edge detection to near-human performance on natural images, their outputs often lack precise localization, a limitation that is particularly harmful in medical applications where millimeter-level accuracy is required. Building on a systematic analysis of ConvNet edge outputs, we propose a medically focused crisp edge detector that adapts a novel top-down backward refinement architecture to medical images (2D and volumetric). Our method progressively upsamples and fuses high-level semantic features with fine-grained low-level cues through a backward refinement pathway, producing high-resolution, well-localized organ boundaries. We further extend the design to handle anisotropic volumes by combining 2D slice-wise refinement with light 3D context aggregation to retain computational efficiency. Evaluations on several CT and MRI organ datasets demonstrate substantially improved boundary localization under strict criteria (boundary F-measure, Hausdorff distance) compared to baseline ConvNet detectors and contemporary medical edge/contour methods. Importantly, integrating our crisp edge maps into downstream pipelines yields consistent gains in organ segmentation (higher Dice scores, lower boundary errors), more accurate image registration, and improved delineation of lesions near organ interfaces. The proposed approach produces clinically valuable, crisp organ edges that materially enhance common medical-imaging tasks.
Abstract (translated)
准确地定位器官边界在医学成像中的分割、配准、手术规划和放射治疗等方面至关重要。虽然深度卷积网络（ConvNets）已经将一般用途的边缘检测提升到了接近人类水平的性能，尤其是在自然图像上，但它们的输出往往缺乏精确的定位能力，在要求毫米级精度的医疗应用中尤其有害。基于对ConvNet边缘输出的系统分析，我们提出了一种以医学为重点的清晰边缘探测器，该探测器采用新颖的自顶向下反向细化架构来处理医学影像（二维和体积图像）。我们的方法通过一个反向细化路径逐步上采样并融合高层次语义特征与低层次细节线索，从而产生高分辨率、精确定位的器官边界。进一步地，我们设计了一个扩展方案，以处理各向异性的体素数据，结合了2D切片级别的优化和轻量级3D上下文聚合，保留计算效率的同时改善效果。 在多个CT和MRI器官数据集上的评估表明，与基线ConvNet检测器及当前的医学边缘/轮廓方法相比，在严格的指标（边界F-measure、Hausdorff距离）下，我们的方法显著提高了边界的定位能力。重要的是，将我们提出的清晰边缘图集成到下游管道中，可以一致地提升器官分割性能（Dice分数更高，边界误差更低），提高图像配准的准确性，并改善邻近器官界面处病变区域的描绘精度。 所提出的方法生成了临床有价值的、清晰的器官边缘，显著增强了常见的医学成像任务。
URL
https://arxiv.org/abs/2508.06805
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COFFEE: A Shadow-Resilient Real-Time Pose Estimator for Unknown Tumbling Asteroids using Sparse Neural Networks
2025-08-05 06:27:14Arion Zimmermann, Soon-Jo Chung, Fred HadaeghAbstract
The accurate state estimation of unknown bodies in space is a critical challenge with applications ranging from the tracking of space debris to the shape estimation of small bodies. A necessary enabler to this capability is to find and track features on a continuous stream of images. Existing methods, such as SIFT, ORB and AKAZE, achieve real-time but inaccurate pose estimates, whereas modern deep learning methods yield higher quality features at the cost of more demanding computational resources which might not be available on space-qualified hardware. Additionally, both classical and data-driven methods are not robust to the highly opaque self-cast shadows on the object of interest. We show that, as the target body rotates, these shadows may lead to large biases in the resulting pose estimates. For these objects, a bias in the real-time pose estimation algorithm may mislead the spacecraft's state estimator and cause a mission failure, especially if the body undergoes a chaotic tumbling motion. We present COFFEE, the Celestial Occlusion Fast FEature Extractor, a real-time pose estimation framework for asteroids designed to leverage prior information on the sun phase angle given by sun-tracking sensors commonly available onboard spacecraft. By associating salient contours to their projected shadows, a sparse set of features are detected, invariant to the motion of the shadows. A Sparse Neural Network followed by an attention-based Graph Neural Network feature matching model are then jointly trained to provide a set of correspondences between successive frames. The resulting pose estimation pipeline is found to be bias-free, more accurate than classical pose estimation pipelines and an order of magnitude faster than other state-of-the-art deep learning pipelines on synthetic data as well as on renderings of the tumbling asteroid Apophis.
Abstract (translated)
空间中未知物体的精确状态估计是一项关键挑战，其应用范围从跟踪太空碎片到估算小型天体的形状。实现这一能力的一个必要条件是能够在连续流图像中找到并追踪特征。现有的方法如SIFT、ORB和AKAZE能够实现实时但不够准确的姿态估计，而现代深度学习方法虽然能提供更高质量的特征，却需要更多的计算资源，这可能在航天器专用硬件上不可用。此外，无论是经典方法还是数据驱动的方法，在面对目标物体上由自身投射出的高度不透明阴影时均不具备鲁棒性。我们展示了随着目标天体旋转，这些阴影可能导致结果姿态估计中的大幅偏差。对于这类天体而言，实时姿态估计算法中的偏置可能会误导航天器的状态估算器，并导致任务失败，特别是在该物体经历混沌翻滚运动的情况下。 我们提出了COFFEE（Celestial Occlusion Fast FEature Extractor），这是一种专门为小行星设计的实时姿态估计框架，旨在利用太阳追踪传感器提供的日相角先验信息。通过将显著轮廓与其投影阴影关联起来，可以检测到一组稀疏但不受阴影移动影响的特征点。接着，一种稀疏神经网络与基于注意力机制的图神经网络配准模型共同训练，以提供连续帧之间的对应关系集合。 实验证明，该姿态估计流程是无偏的、比经典方法更准确，并且在合成数据和翻滚小行星阿波菲斯的渲染图像上，相较于其他最先进的深度学习流程快了一个数量级。
URL
https://arxiv.org/abs/2508.03132
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Single Point, Full Mask: Velocity-Guided Level Set Evolution for End-to-End Amodal Segmentation
2025-08-03 08:36:13Zhixuan Li, Yujia Liu, Chen Hui, Weisi LinAbstract
Amodal segmentation aims to recover complete object shapes, including occluded regions with no visual appearance, whereas conventional segmentation focuses solely on visible areas. Existing methods typically rely on strong prompts, such as visible masks or bounding boxes, which are costly or impractical to obtain in real-world settings. While recent approaches such as the Segment Anything Model (SAM) support point-based prompts for guidance, they often perform direct mask regression without explicitly modeling shape evolution, limiting generalization in complex occlusion scenarios. Moreover, most existing methods suffer from a black-box nature, lacking geometric interpretability and offering limited insight into how occluded shapes are inferred. To deal with these limitations, we propose VELA, an end-to-end VElocity-driven Level-set Amodal segmentation method that performs explicit contour evolution from point-based prompts. VELA first constructs an initial level set function from image features and the point input, which then progressively evolves into the final amodal mask under the guidance of a shape-specific motion field predicted by a fully differentiable network. This network learns to generate evolution dynamics at each step, enabling geometrically grounded and topologically flexible contour modeling. Extensive experiments on COCOA-cls, D2SA, and KINS benchmarks demonstrate that VELA outperforms existing strongly prompted methods while requiring only a single-point prompt, validating the effectiveness of interpretable geometric modeling under weak guidance. The code will be publicly released.
Abstract (translated)
模态分割（amodal segmentation）的目标是恢复物体的完整形状，包括那些被遮挡且没有视觉外观的部分，而传统的分割方法仅关注可视区域。现有的方法通常依赖于强有力的提示，例如可见的掩码或边界框，在实际应用中获取这些提示的成本高昂或不切实际。尽管最近的一些方法，如“Segment Anything Model”（SAM），支持基于点的提示以提供指导，但它们常常直接进行掩码回归，并未显式地建模形状演变过程，这在处理复杂的遮挡场景时限制了其泛化能力。此外，大多数现有的方法具有黑箱特性，缺乏几何解释性，并且对如何推断被遮挡形状提供的见解有限。 为了克服这些局限性，我们提出了VELA（VElocity-driven Level-set Amodal segmentation），这是一种基于速度驱动的层级集模态分割方法，它从点提示出发进行显式的轮廓演变。VELA首先根据图像特征和输入点构建初始层次集合函数，然后在形状特定运动场预测网络提供的指导下逐步演变为最终的全形态掩码。该网络通过端到端学习生成每个步骤的演化动态，这使得基于几何原理的、拓扑灵活的轮廓建模成为可能。 在COCOA-cls、D2SA和KINS等基准测试中的大量实验表明，VELA在仅使用单点提示的情况下优于现有的强提示方法，验证了在弱指导下可解释的几何模型的有效性。该代码将公开发布。
URL
https://arxiv.org/abs/2508.01661
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Spatial-Frequency Aware for Object Detection in RAW Image
2025-08-02 15:03:23Zhuohua Ye, Liming Zhang, Hongru HanAbstract
Direct RAW-based object detection offers great promise by utilizing RAW data (unprocessed sensor data), but faces inherent challenges due to its wide dynamic range and linear response, which tends to suppress crucial object details. In particular, existing enhancement methods are almost all performed in the spatial domain, making it difficult to effectively recover these suppressed details from the skewed pixel distribution of RAW images. To address this limitation, we turn to the frequency domain, where features, such as object contours and textures, can be naturally separated based on frequency. In this paper, we propose Space-Frequency Aware RAW Image Object Detection Enhancer (SFAE), a novel framework that synergizes spatial and frequency representations. Our contribution is threefold. The first lies in the ``spatialization" of frequency bands. Different from the traditional paradigm of directly manipulating abstract spectra in deep networks, our method inversely transforms individual frequency bands back into tangible spatial maps, thus preserving direct physical intuition. Then the cross-domain fusion attention module is developed to enable deep multimodal interactions between these maps and the original spatial features. Finally, the framework performs adaptive nonlinear adjustments by predicting and applying different gamma parameters for the two domains.
Abstract (translated)
基于RAW数据的直接对象检测方法通过利用未经处理的传感器数据（即RAW数据）展现出了巨大的潜力，但同时也面临着固有的挑战。这些挑战主要来源于RAW图像具有宽广的动态范围和线性响应特性，这往往会抑制关键物体细节的有效显示。现有的增强方法几乎都集中在空间域内进行操作，难以从RAW图像中扭曲的像素分布中有效地恢复被压制的关键对象细节。 为了解决这一限制，我们转向了频域，在该领域中可以根据频率自然地分离出特征（如物体轮廓和纹理），这在空间域中较难实现。在此基础上，本文提出了一种新颖的框架——空间-频域感知RAW图像对象检测增强器(SFAE)，它融合了空间与频域表示。 我们的贡献主要体现在三个方面：第一，在“空间化”频率带宽方面，不同于传统深度网络直接操作抽象光谱的方法，我们逆向将各个频率带转换回具体的物理映射（即空间图），从而保持直观的物理理解；第二，开发了一种跨领域融合注意力模块，以促进这些频域生成的空间图与原始空间特征之间的深层多模态交互；第三，在两个域中进行自适应非线性调整：通过预测和应用不同的伽马参数来增强图像质量。 总的来说，SFAE框架旨在利用频域的优势，并结合空间信息，从而提升RAW数据驱动的对象检测性能。
URL
https://arxiv.org/abs/2508.01396
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Occlusion-robust Stylization for Drawing-based 3D Animation
2025-08-01 07:52:07Sunjae Yoon, Gwanhyeong Koo, Younghwan Lee, Ji Woo Hong, Chang D. YooAbstract
3D animation aims to generate a 3D animated video from an input image and a target 3D motion sequence. Recent advances in image-to-3D models enable the creation of animations directly from user-hand drawings. Distinguished from conventional 3D animation, drawing-based 3D animation is crucial to preserve artist's unique style properties, such as rough contours and distinct stroke patterns. However, recent methods still exhibit quality deterioration in style properties, especially under occlusions caused by overlapping body parts, leading to contour flickering and stroke blurring. This occurs due to a `stylization pose gap' between training and inference in stylization networks designed to preserve drawing styles in drawing-based 3D animation systems. The stylization pose gap denotes that input target poses used to train the stylization network are always in occlusion-free poses, while target poses encountered in an inference include diverse occlusions under dynamic motions. To this end, we propose Occlusion-robust Stylization Framework (OSF) for drawing-based 3D animation. We found that while employing object's edge can be effective input prior for guiding stylization, it becomes notably inaccurate when occlusions occur at inference. Thus, our proposed OSF provides occlusion-robust edge guidance for stylization network using optical flow, ensuring a consistent stylization even under occlusions. Furthermore, OSF operates in a single run instead of the previous two-stage method, achieving 2.4x faster inference and 2.1x less memory.
Abstract (translated)
3D动画的目标是从输入图像和目标3D运动序列生成3D动画视频。最近在图像到3D模型方面的发展使得能够直接从用户手绘的草图创建动画成为可能。与传统的3D动画相比，基于绘画的3D动画对于保留艺术家独特的风格属性（如粗糙轮廓和独特的笔触模式）至关重要。然而，尽管取得了这些进展，目前的方法仍然会在风格属性上表现出质量下降的问题，尤其是在由重叠身体部位导致遮挡时，会导致轮廓闪烁和笔迹模糊。这种现象是由于基于绘画的3D动画系统中用于保留绘制样式的风格化网络在训练与推理阶段之间存在的“姿态差距”（stylization pose gap）所引起的。这个姿态差距表示，在进行模型训练时使用的输入目标姿势总是没有遮挡的情况，而在进行推断时遇到的目标姿势则包含由于动态动作而产生的各种遮挡情况。 为此，我们提出了一种名为抗遮挡风格化框架（Occlusion-robust Stylization Framework, OSF）的方法来解决基于绘画的3D动画的问题。我们发现，虽然使用对象边缘作为引导风格化的有效输入先验是有效的，但在推断过程中发生遮挡时其准确性会显著降低。因此，我们的OSF通过利用光流为风格化网络提供抗遮挡的边缘指引，确保即使在存在遮挡的情况下也能保持一致的风格化效果。 此外，与先前采用的两阶段方法不同，OSF可以在单次运行中完成任务，从而实现了2.4倍更快的推理速度和2.1倍更少的记忆消耗。
URL
https://arxiv.org/abs/2508.00398
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Whisker-based Active Tactile Perception for Contour Reconstruction
2025-07-31 07:38:42Yixuan Dang, Qinyang Xu, Yu Zhang, Xiangtong Yao, Liding Zhang, Zhenshan Bing, Florian Roehrbein, Alois KnollAbstract
Perception using whisker-inspired tactile sensors currently faces a major challenge: the lack of active control in robots based on direct contact information from the whisker. To accurately reconstruct object contours, it is crucial for the whisker sensor to continuously follow and maintain an appropriate relative touch pose on the surface. This is especially important for localization based on tip contact, which has a low tolerance for sharp surfaces and must avoid slipping into tangential contact. In this paper, we first construct a magnetically transduced whisker sensor featuring a compact and robust suspension system composed of three flexible spiral arms. We develop a method that leverages a characterized whisker deflection profile to directly extract the tip contact position using gradient descent, with a Bayesian filter applied to reduce fluctuations. We then propose an active motion control policy to maintain the optimal relative pose of the whisker sensor against the object surface. A B-Spline curve is employed to predict the local surface curvature and determine the sensor orientation. Results demonstrate that our algorithm can effectively track objects and reconstruct contours with sub-millimeter accuracy. Finally, we validate the method in simulations and real-world experiments where a robot arm drives the whisker sensor to follow the surfaces of three different objects.
Abstract (translated)
当前，基于触须灵感的触觉传感器在感知领域面临的主要挑战是机器人缺乏对直接接触信息进行主动控制的能力。为了精确地重建物体轮廓，触须传感器需要持续跟踪并保持适当的相对接触姿态。这尤其对于依赖于尖端接触定位的方法来说至关重要，因为这类方法对于锋利表面具有较低的容忍度，并且必须避免进入切向接触状态。 在本文中，我们首先构建了一种磁感应式触须传感器，该传感器配备了由三个灵活螺旋臂组成的紧凑而坚固的悬挂系统。接下来，开发了一种利用已知触须偏转特征直接通过梯度下降法提取尖端接触位置的方法，并应用贝叶斯滤波来减少波动。此外，我们还提出了一种主动运动控制策略，以保持触须传感器相对于物体表面的最佳相对姿态。 为了预测局部曲率并确定传感器的方向，使用了B样条曲线。实验结果表明，我们的算法能够有效地跟踪对象并实现次毫米精度的轮廓重建。最后，在模拟和真实世界的试验中验证了该方法的有效性——在这些试验中，机器人手臂驱动触须传感器沿三个不同物体表面进行接触跟踪。 总的来说，这项研究为利用机械式触觉传感器执行复杂的定位任务提供了一种新的途径，并有助于改善基于直接接触的感知系统。
URL
https://arxiv.org/abs/2507.23305
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RemoteReasoner: Towards Unifying Geospatial Reasoning Workflow
2025-07-25 13:58:11Liang Yao, Fan Liu, Hongbo Lu, Chuanyi Zhang, Rui Min, Shengxiang Xu, Shimin Di, Pai PengAbstract
Remote sensing imagery presents vast, inherently unstructured spatial data, demanding sophisticated reasoning to interpret complex user intents and contextual relationships beyond simple recognition tasks. In this paper, we aim to construct an Earth observation workflow to handle complex queries by reasoning about spatial context and user intent. As a reasoning workflow, it should be somewhat autonomous, where predefined ground-truth reasoning paths do not constrain the learning process. Furthermore, its architecture ought to be unified yet flexible, enabling the model to perform diverse reasoning tasks with distinct output formats through a single forward pass. Existing remote sensing approaches fail to address these requirements, as they rely on supervised fine-tuning paradigms that constrain the autonomy of reasoning. To this end, we propose RemoteReasoner, a flexible and robust workflow for remote sensing reasoning tasks. The design of RemoteReasoner integrates a multi-modal large language model (MLLM) for interpreting user instructions and localizing targets, together with task adaptation strategies that enable multi-granularity output generation. In contrast to existing methods, our framework is trained with reinforcement learning (RL) to endow the MLLM sufficient autonomy for precise reasoning. At the inference stage, our adaptation strategies enable diverse output formats at inference time without requiring task-specific decoders or further fine-tuning. Preliminary experiments demonstrated that RemoteReasoner achieves remarkable performance across multi-granularity reasoning tasks, including region-level and pixel-level. Additionally, our framework enables novel capabilities such as the contour extraction task beyond the reach of existing reasoning pipelines.
Abstract (translated)
遥感影像提供了大量内在无结构的空间数据，这需要复杂的推理来解读用户意图和上下文关系，而这些超出了简单的识别任务范围。本文旨在构建一个地球观测工作流程，用于处理复杂查询并通过推理空间背景和用户意图来解决这些问题。作为一个推理工作流，它应该是某种程度上自主的，即预定义的真实路径不应限制学习过程。此外，其架构应该统一且灵活，能够通过一次前向传播完成多样化的推理任务，并产生不同格式的结果。现有的遥感方法未能满足这些需求，因为它们依赖于监督微调范式，这会限制推理的自主性。为此，我们提出了RemoteReasoner，一种用于遥感推理任务的灵活而稳健的工作流程。 RemoteReasoner的设计集成了一个多模态大型语言模型（MLLM），用于解释用户指令和定位目标，并结合了多粒度输出生成的任务适应策略。与现有方法不同的是，我们的框架通过强化学习（RL）进行训练，赋予MLLM足够的自主性以实现精确推理。在推断阶段，我们的适应策略能够在无需特定任务解码器或进一步微调的情况下，在推断时支持多样化的输出格式。 初步实验表明，RemoteReasoner在多粒度推理任务中实现了显著的性能提升，包括区域级和像素级任务。此外，我们框架还具备现有的推理流水线无法实现的新能力，例如轮廓提取任务。
URL
https://arxiv.org/abs/2507.19280
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Diffusion-FS: Multimodal Free-Space Prediction via Diffusion for Autonomous Driving
2025-07-24 19:30:55Keshav Gupta, Tejas S. Stanley, Pranjal Paul, Arun K. Singh, K. Madhava KrishnaAbstract
Drivable Free-space prediction is a fundamental and crucial problem in autonomous driving. Recent works have addressed the problem by representing the entire non-obstacle road regions as the free-space. In contrast our aim is to estimate the driving corridors that are a navigable subset of the entire road region. Unfortunately, existing corridor estimation methods directly assume a BEV-centric representation, which is hard to obtain. In contrast, we frame drivable free-space corridor prediction as a pure image perception task, using only monocular camera input. However such a formulation poses several challenges as one doesn't have the corresponding data for such free-space corridor segments in the image. Consequently, we develop a novel self-supervised approach for free-space sample generation by leveraging future ego trajectories and front-view camera images, making the process of visual corridor estimation dependent on the ego trajectory. We then employ a diffusion process to model the distribution of such segments in the image. However, the existing binary mask-based representation for a segment poses many limitations. Therefore, we introduce ContourDiff, a specialized diffusion-based architecture that denoises over contour points rather than relying on binary mask representations, enabling structured and interpretable free-space predictions. We evaluate our approach qualitatively and quantitatively on both nuScenes and CARLA, demonstrating its effectiveness in accurately predicting safe multimodal navigable corridors in the image.
Abstract (translated)
可行驶自由空间预测是自动驾驶中的一个基础且关键的问题。近期的工作通过将整个无障碍的路面区域表示为自由空间来解决这个问题。相比之下，我们的目标是估计出驾驶通道，这是整个道路区域内可导航的一个子集。不幸的是，现有的通道估算方法直接假设了一个以鸟瞰图（BEV）为中心的表示方式，这种表示方式难以获得。因此，我们将可行驶自由空间通道预测定义为一个纯粹基于图像感知的任务，并且仅使用单目相机输入来进行这项任务。然而，这样的设定带来了几个挑战，因为没有相应的数据来描述这些存在于图像中的自由空间通道片段。 为了应对这一问题，我们开发了一种新颖的自我监督方法，用于生成自由空间样本，这种方法利用了未来的自车轨迹和前视图相机图像，并且将视觉通道估计过程与自车轨迹联系起来。接着，我们使用扩散过程来建模这些在图像中的片段分布情况。 然而，现有的基于二值掩码表示方式的方法存在许多限制。因此，我们引入了一种名为ContourDiff的专门化架构，这是一种基于扩散技术的设计，在降噪时采用轮廓点而非依赖于传统的二进制掩码表示方法，从而能够生成结构清晰且易于解释的自由空间预测结果。 我们在nuScenes和CARLA数据集上对我们的方法进行了定性和定量评估，展示了其在准确预测图像中安全多模态可导航通道方面的能力。
URL
https://arxiv.org/abs/2507.18763
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HoliTracer: Holistic Vectorization of Geographic Objects from Large-Size Remote Sensing Imagery
2025-07-22 05:55:00Yu Wang, Bo Dang, Wanchun Li, Wei Chen, Yansheng LiAbstract
With the increasing resolution of remote sensing imagery (RSI), large-size RSI has emerged as a vital data source for high-precision vector mapping of geographic objects. Existing methods are typically constrained to processing small image patches, which often leads to the loss of contextual information and produces fragmented vector outputs. To address these, this paper introduces HoliTracer, the first framework designed to holistically extract vectorized geographic objects from large-size RSI. In HoliTracer, we enhance segmentation of large-size RSI using the Context Attention Net (CAN), which employs a local-to-global attention mechanism to capture contextual dependencies. Furthermore, we achieve holistic vectorization through a robust pipeline that leverages the Mask Contour Reformer (MCR) to reconstruct polygons and the Polygon Sequence Tracer (PST) to trace vertices. Extensive experiments on large-size RSI datasets, including buildings, water bodies, and roads, demonstrate that HoliTracer outperforms state-of-the-art methods. Our code and data are available in this https URL.
Abstract (translated)
随着遥感影像（RSI）分辨率的提高，大尺寸的遥感影像已成为地理对象高精度矢量化映射的重要数据来源。现有的方法通常局限于处理小图像块，这往往会导致上下文信息的丢失，并产生碎片化的矢量输出结果。为了解决这些问题，本文介绍了HoliTracer，这是首个设计用于从大尺寸RSI中全面提取向量化地理对象的框架。 在HoliTracer中，我们通过使用包含局部到全局注意力机制以捕捉上下文依赖关系的Context Attention Net（CAN），增强了对大尺寸遥感影像的分割。此外，我们利用了一个稳健的工作流程来实现整体矢量化，该工作流程采用Mask Contour Reformer（MCR）重构多边形，并通过Polygon Sequence Tracer（PST）追踪顶点。 在包括建筑物、水域和道路的大尺寸RSI数据集上进行的广泛实验表明，HoliTracer优于当前最先进的方法。我们的代码和数据可以在[此处](https://这个URL)获取。
URL
https://arxiv.org/abs/2507.16251
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