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Fisheye cameras, with their ultra-wide field of view, offer significant benefits for depth estimation in applications such as autonomous navigation, robotics, and immersive imaging by capturing more scene content from a single viewpoint. However, their strong radial distortion and varying spatial resolution …
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Jiakai Lin, Jinchang Zhang, Ge Jin, Wen‐Zhan Song et autres
Plant roots typically exhibit a highly complex and dense architecture, incorporating numerous slender lateral roots and branches, which significantly hinders the precise capture and modeling of the entire root system. Additionally, roots often lack sufficient texture and color information, making it difficult …
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Z.G. Li, Jinchang Zhang, Ming Zhang, Guoyu Lu
We propose an automated genomic interpretation module that transforms raw DNA sequences into actionable, interpretable decisions suitable for integration into medical automation and robotic systems. Our framework combines Chaos Game Representation (CGR) with a Concept Bottleneck Model (CBM), enforcing predictions to flow …
Jiakai Lin, Jinchang Zhang, Guoyu Lu
With growing demand for interpretability in deep learning, especially in high stakes domains, Concept Bottleneck Models (CBMs) address this by inserting human understandable concepts into the prediction pipeline, but they are generally single modal and ignore structured concept relationships. To overcome these …
Depth estimation is a core problem in robotic perception and vision tasks, but 3D reconstruction from a single image presents inherent uncertainties. Current depth estimation models primarily rely on inter-image relationships for supervised training, often overlooking the intrinsic information provided by the …
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Scene reconstruction has a wide range of applications in computer vision and robotics. To build practical constraints and feature correspondences, rich textures and distinguished gradient variations are particularly required in classic and learning-based SfM. When building low-texture regions with repeated patterns, especially …
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Deep neural networks (DNNs) have gained significant attention in 3D object reconstruction. However, detecting and reconstructing hidden or buried objects underground remains a challenging task. Ground Penetrating Radar (GPR) has emerged as a cost-effective and non-destructive technology for subsurface object detection, including …
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Jiakai Lin, Jinchang Zhang, Guoyu Lu
Keypoint detection and local feature description are fundamental tasks in robotic perception, critical for applications such as SLAM, robot localization, feature matching, pose estimation, and 3D mapping. While existing methods predominantly operate on RGB images, we propose a novel network that directly …
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Gas concentration estimation is crucial for understanding and mitigating climate change. While most research and monitoring efforts focus on major greenhouse gases such as CO2, significantly less attention has been given to trace gases like NO2, which play a critical role in …
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Depth estimation is a core problem in robotic perception and vision tasks, but 3D reconstruction from a single image presents inherent uncertainties. Current depth estimation models primarily rely on inter-image relationships for supervised training, often overlooking the intrinsic information provided by the …
Jiakai Lin, Jinchang Zhang, Guoyu Lu
Keypoint detection and local feature description are fundamental tasks in robotic perception, critical for applications such as SLAM, robot localization, feature matching, pose estimation, and 3D mapping. While existing methods predominantly operate on RGB images, we propose a novel network that directly …
Jinchang Zhang, Praveen Kumar Reddy, Xue-Iuan Wong, Yiannis Aloimonos et autres
Depth estimationn is a critical topic for robotics and vision-related tasks. In monocular depth estimation, in comparison with supervised learning that requires expensive ground truth labeling, self-supervised methods possess great potential due to no labeling cost. However, self-supervised learning still has a …
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