Jul 2026· International Conference on Image Processing and Intelligent Control· Vol 14262, pp. 142620K - 142620K-7· 0 citations· 11 references
Engineering
TL;DR
This study presents an enhanced YOLOv11 framework specifically designed for global geometric perception and high-fidelity single-stage 6D pose regression, which validate that the integration of global geometric awareness consistently outperforms the vanilla YOLOv11 and other classical baselines in complex scenarios.
Abstract
Precise 6D object pose estimation from RGB images remains a formidable challenge due to complex backgrounds and severe occlusions. To address these issues, our study presents an enhanced YOLOv11 framework specifically designed for global geometric perception and high-fidelity single-stage 6D pose regression. The core of our architecture is the C3k2SW module, which innovatively synergizes local convolutional features with global long-range dependencies through windowbased self-attention, significantly enhancing the network's geometric perception of spatial topologies. Furthermore, to optimize multi-scale feature interaction, an adaptive ConcatA module and a Bi-directional Feature Pyramid Attention Network (BFPAN) are proposed to suppress background noise while preserving fine-grained geometric details across different scales. Experimental results on the LineMod benchmark demonstrate that our method achieves an optimal tradeoff between inference efficiency and accuracy, reaching an average ADD(-S) accuracy of 76.50% and 84.92% on the 5cm 5° metric, respectively. These results validate that the integration of global geometric awareness consistently outperforms the vanilla YOLOv11 and other classical baselines in complex scenarios.
Small object detection in crowded urban and aerial scenes remains a critical challenge due to limited pixel information and information loss in deep neural networks. This study introduces a novel optimization framework for YOLOv11, specifically engineered for tiny-scale targets by integrating convolutional block attention modules (CBAM), k-means anchor clustering, and an enhanced feature pyramid network (FPN). Evaluated on the TinyPerson and COCO-mini datasets, the YOLOv11-optimized model achieves significant performance breakthroughs, delivering a +7.3% gain in mean average precision (mAP) and a +10.5% increase in recall over the baseline. Notably, the model achieved a recall of 0.072 on the TinyPerson dataset, with double sensitivity of standard YOLOv11. With a high-speed inference rate of 27.3 FPS, this research demonstrates that strategic architectural refinements can drastically improve small object detection reliability without compromising real-time viability on edge devices.
Husna Sarirah Husin, H. Hao, Yuan-Fei Pan et al.· IAES International Journal o...· 0 citations
Conditional flow matching has enabled a step forward in object 6D pose estimation, achieving state-of-the-art performance by progressively denoising and registering object representations to observed scenes. Existing methods require training task-specific encoders supervised on object-scene overlap and rely on trivial feature fusion strategies to resolve pose ambiguities. We present FunFlow6D, a novel flow matching-based formulation that leverages features from geometric and appearance foundation models for pose estimation, eliminating the need for task-specific encoder training. We also introduce a cross attention-based fusion mechanism that dynamically combines geometric and appearance features to provide richer conditioning for the flow matching module. Experiments on four datasets from the BOP benchmark show that FunFlow6D outperforms the previous state of the art while reducing supervision requirements and memory overhead. Extensive ablations validate the contribution of each proposed component. Project website: https://tev-fbk.github.io/FunFlow6D/.
Amir Hamza, Davide Boscaini, Fabio Poiesi· 0 citations
A 3D Local- Global Linear Attention Mechanism (LG-LAM) is devised that efficiently captures long-range contextual information with linear complexity, enabling a comprehensive understanding of the 3D scene without heavy computational burdens.
Jie Li, Jiaheng Xu, Laiyan Ding et al.· International Conference on...· 0 citations
This paper studies monocular 6D pose estimation of small cubic objects from a single RGB image and proposes a two-stage manipulation- oriented framework, which achieves the strongest overall balance in ADD-S, translation accuracy, rotation stability, and task-oriented usability metrics.
Xinmiao Du· Poster Volume 0007 The 2026...· 0 citations
Achieving accurate and efficient object pose estimation is a key goal in computer vision. Most existing methods rely on controlled environments, limiting their effectiveness in complex, dynamic, and unstructured real-world scenarios, especially for novel objects, severe occlusion, or sensor noise. Recent studies show that integrating multimodal information can improve adaptability and accuracy, with RGB-D fusion gaining attention due to its low cost and accessibility. However, effectively combining geometric and semantic cues from RGB and depth particularly at local regions remains challenging. To address this, we propose a Co-attention Refinement Network using a neighborhood-weighted voting mechanism to construct collaborative attention for efficient RGB-depth fusion. A non-iterative global-local context aggregation strategy further mitigates fusion errors from modality differences and local inconsistencies. The key components include Co-attention Alignment and Fusion (CCAF) and Enhanced Global-Local Context Aggregation (EGCA), which enable adaptive feature alignment and fine-grained context modeling. Extensive experiments on LINEMOD, Occlusion-LineMOD, and YCB-Video demonstrate the effectiveness and generalization of our approach. Note to Practitioners—This paper addresses the challenge of accurate and robust 6D object pose estimation in real-world environments, where factors such as poor lighting, occlusion, background clutter, and sensor noise commonly affect perception systems. Existing approaches relying on either RGB or depth information alone often fail when faced with textureless or reflective objects. To overcome these limitations, we propose a multimodal fusion framework that exploits the complementary strengths of RGB and depth modalities through a novel neighbor-based cross attention mechanism. By adaptively integrating information across neighboring regions, our method enhances the reliability of feature fusion and reduces the negative impact of noisy or misleading background data. Additionally, we introduce a confidence estimation module to assess the quality of the predicted poses, which is useful for practical robotic applications that require high reliability. This system is particularly relevant for tasks such as robotic grasping, assembly, and object manipulation in industrial and service settings. Practitioners interested in deploying multimodal perception systems can benefit from the proposed lightweight and generalizable design that supports improved performance in cluttered, dynamic, or previously unseen environments.
Hui Zhang, Yue Wang, Jianhao Jiao et al.· IEEE Transactions on Automat...· 0 citations
High-precision perception is fundamental to safe autonomous driving, and BEV-based 3D object detection via lidar-camera fusion plays a crucial role in improving detection accuracy and robustness. To address insufficient feature representation, spatial misalignment, and the limitations of static fusion strategies, this work develops a multi-modal collaborative optimization framework: (1)A depth uncertainty-guided feature modulation method is proposed, in which depth entropy and variance are jointly modeled to generate a BEV alignment confidence map, enabling adaptive enhancement and suppression of image features and effectively mitigating cross-modal alignment errors. (2) We propose geometric saliency pillar feature encoding, which enhances point cloud structural representation via point-wise saliency weighting and multi-statistic aggregation. (3)We design an adaptive interaction fusion strategy that explicitly models crossmodal consistency and discrepancy relationships, generating adaptive weights to achieve dynamic fusion of multimodal features. Experiments on the NuScenes benchmark and a self-collected T23 dataset show that mAP improves from 62.1% to 63.1% and from 68.8% to 70.1%, respectively, with NDS reaching 69.6 and 75.6. Meanwhile, mATE, mASE, and mAOE consistently decrease, demonstrating that the proposed modules provide differentiated contributions and exhibit strong synergistic effects in performance optimization.
Jie Hu, Xinghao Cheng, Shuaidi He et al.· International Conference on...· 0 citations
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