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版本更新 · v4arXiv:2604.08410

BLaFA: Bridging Language to 3D Functional Affordances for Dexterous Robotic Grasping in Tool Use

Fan Yang, Wenrui Chen, Guorun Yan, Ruize Liao, Wanjun Jia, Dongsheng Luo, Jianwen Hu, Jiacheng Lin 等 11 位作者

原文摘要

In industrial assembly, equipment maintenance, and everyday tool-use scenarios, robots need not only to grasp objects stably but also to establish task-appropriate contact configurations according to task intentions and tool functionality. However, existing methods lack explicit mappings from open-vocabulary instructions to 3D functional contact structures and dexterous grasp configurations, limiting robots' adaptability to diverse tools and functional tasks. To address this limitation, we propose BLaFA (Bridging Language to 3D Functional Affordances for Dexterous Robotic Grasping), a structured framework for language-driven functional dexterous grasping. First, a Knowledge-guided Language Parsing (KLP) module leverages structured knowledge to transform natural-language instructions into semantic sextuples of manipulation constraints. Second, a 3D Gaussian Splatting based hierarchical semantic representation and a Triangular Functional Point Localization (TriLocation) module are developed to localize 3D functional contact keypoints under task-tool topology constraints. Finally, a 3D Keypoint Grasp Matrix Transformation (KGT3D+) module maps the localized keypoints and semantic constraints to executable wrist poses and finger-level joint and force commands. Evaluations on 100 open-vocabulary language instructions, 14 task-tool combinations covering four functional intents, and real-world robotic experiments in representative scenes demonstrate that BLaFA can effectively translate natural-language instructions into task-aligned 3D functional contact structures and corresponding dexterous grasps, thereby enabling language-driven robotic tool use across diverse tasks. The source code will be made publicly available. Code will be publicly available at https://github.com/PopeyePxx/BLaFA.

cs.CV · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12674

What Breaks Scribble-Supervised Segmentation? An Audit-Informed Benchmark for Scribble Annotation Robustness

Minheng Wu, Di Wen, Ruiping Liu, Junwei Zheng, Yufan Chen, Kailun Yang, Kunyu Peng

原文摘要

Scribble-supervised segmentation is typically evaluated with a fixed annotation set, leaving robustness to distinct semantic and geometric imperfections poorly understood. Motivated by an audit of 12,031 manual PASCAL Visual Object Classes (VOC) 2012 scribbles and prior work, we introduce six controlled perturbation operators. Class corruption, spatial offset, and directional perturbation directly operationalize audit signals. Stroke-length reduction is motivated by prior scribble studies, while random dropout and fragmentation serve as diagnostic controls. Each operator reports requested and achieved changes. We evaluate manual and automatic scribbles across two image domains, two principal backbone settings, and multiple training pipelines, with an additional MiT-B1 PFA anchor evaluation. In the five-seed Tree Energy Loss (TEL)-ResNet101 VOC study, mean intersection over union (mIoU) drops from 70.08 to 1.46 under class corruption, compared with 35.57 for spatial offset, 52.20 for directional perturbation, and 63.24 for stroke-length reduction at their strongest settings. At the evaluated endpoints, semantic-anchor corruption produces the largest degradation, while geometric and support responses depend on the pipeline and dataset. Our benchmark provides a reproducible protocol for diagnosing annotation robustness beyond clean-set accuracy. The operator code is available at https://github.com/Wmhiram99/scribble-operator-benchmark.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.10009

Towards Multi-Source Domain Generalization for Sleep Staging with Noisy Labels

Kening Wang, Di Wen, Yufan Chen, Ruiping Liu, Junwei Zheng, Jiale Wei, Kailun Yang, Rainer Stiefelhagen 等 9 位作者

原文摘要

Automatic sleep staging is a multimodal learning problem involving heterogeneous physiological signals such as EEG and EOG, which often suffer from domain shifts across institutions, devices, and populations. In practice, these data are also affected by noisy annotations, yet label-noise-robust multi-source domain generalization remains underexplored. We present the first benchmark for Noisy Labels in Multi-Source Domain-Generalized Sleep Staging (NL-DGSS) and show that existing noisy-label learning methods degrade substantially when domain shifts and label noise coexist. To address this challenge, we propose FF-TRUST, a domain-invariant multimodal sleep staging framework with Joint Time-Frequency Early Learning Regularization (JTF-ELR). By jointly exploiting temporal and spectral consistency together with confidence-diversity regularization, FF-TRUST improves robustness under noisy supervision. Experiments on five public datasets demonstrate consistent state-of-the-art performance under diverse symmetric and asymmetric noise settings.

cs.LG · cs.CV · cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2503.00747

LFX: Fusion-Form-Flexible Light Field Modeling for Dense Semantic Segmentation and Salient Object Detection

Fei Teng, Lingxin Huang, Buyin Deng, Kai Luo, Boyuan Zheng, Zheng Fang, Hong Zheng, Kunyu Peng 等 11 位作者

原文摘要

Two distinct light field (LF) observation forms, namely sub-aperture images (S.Aperture images) and focal stack (FS), correspond to two fusion settings: the central image with S.Aperture images (CS) and the central image with FS (CF). Existing LF perception methods are typically form-specific, while a modeling design that can be separately instantiated under either setting remains underexplored. To address this gap, we present LFX, a form-flexible LF perception framework. Here, ``X'' represents the LF observation form, indicating that the one modeling design can be separately instantiated under two fusion settings (CS and CF). Specifically, we propose Field-of-Parallax Angular Subspace Modeling (FoP-ASM), which maps the feature difference between each auxiliary observation and the central image to an observation-specific angular marker. This avoids pairwise modeling among auxiliary observations and form-specific geometric assumptions. Meanwhile, a shared manifold subspace and the associated regularization losses constrain the angular residuals within a common feature space, promoting semantic consistency across auxiliary observations. Extensive evaluations across three LF datasets show that LFX achieves state-of-the-art results across distinct LF forms. Under the S.Aperture images and FS settings, respectively, LFX reduces MAE by 12.12%/20.59% on DUTLF-V2 and by 6.15%/7.69% on HFUT, while achieving the best performance on UrbanLF-real. The source code will be made publicly available at https://github.com/FeiT-FeiTeng/LFX.

cs.CV · cs.RO · eess.IV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.12297

EgoEV-HandPose: Egocentric 3D Hand Pose Estimation and Gesture Recognition with Stereo Event Cameras

Luming Wang, Hao Shi, Jiajun Zhai, Kailun Yang, Kaiwei Wang

原文摘要

Egocentric 3D hand pose estimation and gesture recognition are fundamental to understanding hand interactions from first-person observations. Robust perception remains challenging: frame-based cameras suffer from motion blur and limited dynamic range, while event-based approaches face ego-motion interference, monocular depth ambiguity, and limited real-world datasets. We propose EgoEV-HandPose, an end-to-end framework for joint 3D bimanual hand pose estimation and gesture recognition from stereo event streams. Its core module, KeypointBEV, combines sparse 3D joint hypotheses with geometry-aware stereo fusion and iterative reprojection-guided refinement to progressively resolve depth uncertainty and improve pose accuracy. We also introduce EgoEVHands, the first large-scale real-world stereo event dataset for egocentric 3D bimanual hand pose estimation and gesture recognition. Built upon EgoEvGesture recordings, it adds dense 2D/3D hand annotations and comprises 2,498 annotated stereo sequence pairs across 38 gesture classes. Extensive experiments demonstrate that EgoEV-HandPose achieves state-of-the-art performance on EgoEVHands, obtaining a mean per-joint position error (MPJPE) of 30.54mm and a gesture recognition accuracy of 86.87%, significantly outperforming RGB-based stereo and previous event-based methods. The framework exhibits exceptional robustness under low illumination and severe bimanual occlusion. Experiments on DHP19 further demonstrate the cross-dataset and cross-architecture generalizability of the KeypointBEV refinement design. Dataset and code will be released at https://github.com/ZJUWang01/EgoEV-HandPose.

cs.CV · cs.RO · eess.IV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2606.30097

CylindTrack: Depth-Aware Cylindrical Motion Modeling for Panoramic Multi-Object Tracking in Mobile Robots

Buyin Deng, Kai Luo, Lingxin Huang, Xinqi Liu, Fei Cheng, Hang Zheng, Liming Yin, Kailun Yang

原文摘要

Maintaining consistent target identities and observation histories is essential for mobile robot perception. Panoramic Multi-Object Tracking (MOT) benefits from broad visual coverage but must address coordinate discontinuities at the equirectangular image seam and fluctuations in relative-depth estimates. Such fluctuations can undermine depth-aware association, highlighting the need for temporally consistent depth cues. We propose CylindTrack, a depth-aware cylindrical tracking-by-detection framework that jointly models panoramic topology and temporal depth consistency. Depth-Temporal Trajectory Modeling (DTM) maintains instance depth and its rate of change as filtered trajectory states. Spherical Spatio-Temporal Consistency Learning (SSTC) combines a Temporal Mixer with Spherical Geometry-aware Attention to refine depth-aware representations. A Topology-Aware Cylindrical Motion Model (TCMM) represents horizontal motion in an unwrapped angular state and incorporates periodic overlap, angular consistency, and filtered depth cues into association. Experiments on QuadTrack and JRDB demonstrate improved identity consistency and trajectory continuity, with HOTA scores of 33.67 and 31.12 and IDF1 scores of 40.45 and 34.33, respectively. The framework processes 28.56 and 21.34 frames per second, respectively, on an NVIDIA GeForce RTX 3090 GPU under the reported evaluation settings. These results support persistent panoramic multi-object tracking for mobile robot perception. The source code will be released at https://github.com/warriordby/CylindTrack.

cs.CV · cs.RO · eess.IV

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arXiv ↗PDF ↗