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arXiv 2023年

作者： Ye, Yaozu Shi, Hao Yang, Kailun Wang, Ze Yin, Xiaoting Lin, Yining Liu, Mao Wang, Yaonan Wang, Kaiwei The State Key Laboratory of Modern Optical Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Shanghai SUPREMIND Technology Co. Ltd Shanghai201210 China Dongguan Yutong Optical Technology Co. Ltd Dongguan523866 China

Optical flow estimation is a fundamental task in the field of autonomous driving. Event cameras are capable of responding to log-brightness changes in microseconds. Its characteristic of producing responses only to the changing region is particularly suitable for optical flow estimation. In contrast to the super low-latency response speed of event cameras, existing datasets collected via event cameras, however, only provide limited frame rate optical flow ground truth, (e.g., at 10Hz), greatly restricting the potential of event-driven optical flow. To address this challenge, we put forward a high-frame-rate, low-latency event representation Unified Voxel Grid, sequentially fed into the network bin by bin. We then propose EVA-Flow, an EVent-based Anytime Flow estimation network to produce high-frame-rate event optical flow with only low-frame-rate optical flow ground truth for supervision. The key component of our EVA-Flow is the stacked Spatiotemporal Motion Refinement (SMR) module, which predicts temporally dense optical flow and enhances the accuracy via spatial-temporal motion refinement. The time-dense feature warping utilized in the SMR module provides implicit supervision for the intermediate optical flow. Additionally, we introduce the Rectified Flow Warp Loss (RFWL) for the unsupervised evaluation of intermediate optical flow in the absence of ground truth. This is, to the best of our knowledge, the first work focusing on anytime optical flow estimation via event cameras. A comprehensive variety of experiments on MVSEC, DESC, and our EVA-FlowSet demonstrates that EVA-Flow achieves competitive performance, super-low-latency (5ms), fastest inference (9.2ms), time-dense motion estimation (200Hz), and strong generalization. Our code will be available at https://***/Yaozhuwa/EVA-Flow. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical flows

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Representing Domain-Mixing Optical Degradation for Real-World Computational Aberration Correction via Vector Quantization

arXiv

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arXiv 2024年

作者： Jiang, Qi Yi, Zhonghua Gao, Shaohua Gao, Yao Qian, Xiaolong Shi, Hao Sun, Lei Niu, JinXing Wang, Kaiwei Yang, Kailun Bai, Jian State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou310027 China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Intelligent Optics & Photonics Research Center Jiaxing Research Institute Zhejiang University Jiaxing314031 China School of Mechanical Engineering North China University of Water Resources and Electric Power Zhenzhou450045 China

Relying on paired synthetic data, existing learning-based Computational Aberration Correction (CAC) methods are confronted with the intricate and multifaceted synthetic-to-real domain gap, which leads to suboptimal performance in real-world applications. In this paper, in contrast to improving the simulation pipeline, we deliver a novel insight into real-world CAC from the perspective of Unsupervised Domain Adaptation (UDA). By incorporating readily accessible unpaired real-world data into training, we formalize the Domain Adaptive CAC (DACAC) task, and then introduce a comprehensive Real-world aberrated images (Realab) dataset to benchmark it. The setup task presents a formidable challenge due to the intricacy of understanding the target optical degradation domain. To this intent, we propose a novel Quantized Domain-Mixing Representation (QDMR) framework as a potent solution to the issue. centering around representing and quantizing the optical degradation which is consistent across different images, QDMR adapts the CAC model to the target domain from three key aspects: (1) reconstructing aberrated images of both domains by a VQGAN to learn a Domain-Mixing Codebook (DMC) characterizing the optical degradation;(2) modulating the deep features in CAC model with DMC to transfer the target domain knowledge;and (3) leveraging the trained VQGAN to generate pseudo target aberrated images from the source ones for convincing target domain supervision. Extensive experiments on both synthetic and real-world benchmarks reveal that the models with QDMR consistently surpass the competitive methods in mitigating the synthetic-to-real gap, which produces visually pleasant real-world CAC results with fewer artifacts. Codes and datasets are made publicly available at https://***/zju-jiangqi/QDMR. Copyright © 2024, The Authors. All rights reserved.

关键词： Vector quantization

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When Shannon Meets Nonstochastic Information: Communication and Estimation

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IEEE Transactions on Automatic control 2025年

作者： Zhang, Wentao Zuo, Zhiqiang Zhao, Rui Wang, Yijing Hu, Guoqiang Zhang, Hui Wang, Yaonan Hunan University School of Robotics Changsha410082 China Nanyang Technological University Continental-NTU Corporate Lab Singapore639798 Singapore Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin300072 China Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 Singapore National Engineering Research Center for Robot Visual Perception and Control Changsha410082 China Hunan University College of Electrical and Information Engineering Changsha410082 China

This notes focuses on the issue of whether or not the state of a plant can be estimated when the inputs of communication channel involve noise and disturbance simultaneously. It turns out to calculate the maximum capacity of stationary memoryless multiple quasi-access channel under nonstochastic information theory. It is shown that the maximum information rate over such a channel is determined by a convex hull spanned by the zero-error capacities which are specified on the underlying sample spaces. Moreover, the state of a plant could be estimated provided that the maximal information rate is strictly greater than the topology entropy. © 1963-2012 IEEE.

关键词： Communication channels (information theory)

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Beyond the Field-of-View: Enhancing Scene Visibility and perception with Clip-Recurrent Transformer

arXiv

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arXiv 2022年

作者： Shi, Hao Jiang, Qi Yang, Kailun Yin, Xiaoting Wang, Ze Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation the National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Shanghai SUPREMIND Technology Company Ltd Shanghai201210 China

Vision sensors are widely applied in vehicles, robots, and roadside infrastructure. However, due to limitations in hardware cost and system size, camera Field-of-View (FoV) is often restricted and may not provide sufficient coverage. Nevertheless, from a spatiotemporal perspective, it is possible to obtain information beyond the camera’s physical FoV from past video streams. In this paper, we propose the concept of online video inpainting for autonomous vehicles to expand the field of view, thereby enhancing scene visibility, perception, and system safety. To achieve this, we introduce the FlowLens architecture, which explicitly employs optical flow and implicitly incorporates a novel clip-recurrent transformer for feature propagation. FlowLens offers two key features: 1) FlowLens includes a newly designed Clip-Recurrent Hub with 3D-Decoupled Cross Attention (DDCA) to progressively process global information accumulated over time. 2) It integrates a multi-branch Mix Fusion Feed Forward Network (MixF3N) to enhance the precise spatial flow of local features. To facilitate training and evaluation, we derive the KITTI360 dataset with various FoV mask, which covers both outer- and inner FoV expansion scenarios. We also conduct both quantitative assessments and qualitative comparisons of beyond-FoV semantics and beyond-FoV object detection across different models. We illustrate that employing FlowLens to reconstruct unseen scenes even enhances perception within the field of view by providing reliable semantic context. Extensive experiments and user studies involving offline and online video inpainting, as well as beyond-FoV perception tasks, demonstrate that FlowLens achieves state-of-the-art performance. The source code and dataset are made publicly available at https://github. com/MasterHow/FlowLens. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical flows

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Offboard Occupancy Refinement with Hybrid Propagation for Autonomous Driving

arXiv

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arXiv 2024年

作者： Shi, Hao Wang, Song Zhang, Jiaming Yin, Xiaoting Wang, Zhongdao Wang, Guangming Zhu, Jianke Yang, Kailun Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The College of Computer Science and Technology Zhejiang University Hangzhou310027 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany Huawei Noah’s Ark Lab. Canada The Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom

Vision-based occupancy prediction, also known as 3D Semantic Scene Completion (SSC), presents a significant challenge in computer vision. Previous methods, confined to onboard processing, struggle with simultaneous geometric and semantic estimation, continuity across varying viewpoints, and single-view occlusion. Our paper introduces OccFiner, a novel offboard framework designed to enhance the accuracy of vision-based occupancy predictions. OccFiner operates in two hybrid phases: 1) a multi-to-multi local propagation network that implicitly aligns and processes multiple local frames for correcting onboard model errors and consistently enhancing occupancy accuracy across all distances. 2) the region-centric global propagation, focuses on refining labels using explicit multi-view geometry and integrating sensor bias, especially to increase the accuracy of distant occupied voxels. Extensive experiments demonstrate that OccFiner improves both geometric and semantic accuracy across various types of coarse occupancy, setting a new state-of-the-art performance on the SemanticKITTI dataset. Notably, OccFiner elevates vision-based SSC models to a level even surpassing that of LiDAR-based onboard SSC models. Furthermore, OccFiner is the first to achieve automatic annotation of SSC in a purely vision-based approach. Quantitative experiments prove that OccFiner successfully facilitates occupancy data loop-closure in autonomous driving. Additionally, we quantitatively and qualitatively validate the superiority of the offboard approach on city-level SSC static maps. The source code will be made publicly available at https://***/MasterHow/OccFiner. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Steady Tracker: Tracking a Target Stably Using a Quadrotor

Steady Tracker: Tracking a Target Stably Using a Quadrotor

引用

IEEE International Conference on robotics and Biomimetics

作者： Hongwen Li Hang Zhong Yongsheng Lv Jianjun Sha Yu Long Yaonan Wang Harbin Engineering University Harbin Qingdao Innovation and Development Center of Harbin Engineering University Qingdao China School of Robotics National Engineering Research Center for Robot Visual Perception and Control Hunan University College of Electrical and Information Engineering Hunan University Changsha China

ISBN: (纸本)9781665481106

Maneuvering target tracking of Unmanned Aerial Vehicle(UAV) in cluttered environments is a challenging issue owing to the unknown motion intention of the target and the complex moving environments. As the complexity of the environment increases, stable and secure target tracking is increasingly difficult to guarantee. To address the issue, this paper proposes a stable quadrotor tracking solution. The proposed solution contains two parts: target motion prediction and tracking path searching. The target motion prediction method predicts the future target motion based on the obtained target observations while considering observation noise and prediction errors. The tracking path searching method utilizes a sampling-based search method, using the homotopy of paths to ensure that the tracking path and the target position are in the same space. Finally, simulations, real-world experiments and statistical analysis verify the correctness and effectiveness of the proposed approach.

关键词： Target tracking Statistical analysis Prediction methods Search problems Turning Prediction algorithms Convex functions

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Design, analysis, and manufacturing of a glass-plastic hybrid minimalist aspheric panoramic annular lens

arXiv

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arXiv 2024年

作者： Gao, Shaohua Jiang, Qi Liao, Yiqi Qiu, Yi Ying, Wanglei Yang, Kailun Wang, Kaiwei Zhang, Benhao Bai, Jian State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou310027 China Ningbo Lian Technology Co. Ltd Ningbo Lian Ningbo315500 China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Intelligent Optics & Photonics Research Center Jiaxing Research Institute Zhejiang University Jiaxing314031 China Central Research Institue of Sunny Optical Technology Sunny Optical Technology Hangzhou311215 China

We propose a high-performance glass-plastic hybrid minimalist aspheric panoramic annular lens (ASPAL) to solve several major limitations of the traditional panoramic annular lens (PAL), such as large size, high weight, and complex system. The field of view (FoV) of the ASPAL is 360◦×(35◦∼110◦) and the imaging quality is close to the diffraction limit. This large FoV ASPAL is composed of only 4 lenses. Moreover, we establish a physical structure model of PAL using the ray tracing method and study the influence of its physical parameters on compactness ratio. In addition, for the evaluation of local tolerances of annular surfaces, we propose a tolerance analysis method suitable for ASPAL. This analytical method can effectively analyze surface irregularities on annular surfaces and provide clear guidance on manufacturing tolerances for ASPAL. Benefiting from high-precision glass molding and injection molding aspheric lens manufacturing techniques, we finally manufactured 20 ASPALs in small batches. The weight of an ASPAL prototype is only 8.5 g. Our framework provides promising insights for the application of panoramic systems in space and weight-constrained environmental sensing scenarios such as intelligent security, micro-UAVs, and micro-robots. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical design

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Towards Source-free Domain Adaptive Semantic Segmentation via Importance-aware and Prototype-contrast Learning

arXiv

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arXiv 2023年

作者： Cao, Yihong Zhang, Hui Lu, Xiao Xiao, Zheng Yang, Kailun Wang, Yaonan College of Computer Science and Electronic Engineering Hunan University Changsha410082 China National Engineering Research Center of Robot Vision Perception and Control Technology School of Robotics Hunan University Changsha410082 China College of Engineering and Design Hunan Normal University Changsha410082 China

Domain adaptive semantic segmentation enables robust pixel-wise understanding in real-world driving scenes. Source-free domain adaptation, as a more practical technique, addresses the concerns of data privacy and storage limitations in typical unsupervised domain adaptation methods, making it especially relevant in the context of intelligent vehicles. It utilizes a well-trained source model and unlabeled target data to achieve adaptation in the target domain. However, in the absence of source data and target labels, current solutions cannot sufficiently reduce the impact of domain shift and fully leverage the information from the target data. In this paper, we propose an end-to-end source-free domain adaptation semantic segmentation method via Importance-Aware and Prototype-Contrast (IAPC) learning. The proposed IAPC framework effectively extracts domain-invariant knowledge from the well-trained source model and learns domain-specific knowledge from the unlabeled target domain. Specifically, considering the problem of domain shift in the prediction of the target domain by the source model, we put forward an importance-aware mechanism for the biased target prediction probability distribution to extract domain-invariant knowledge from the source model. We further introduce a prototype-contrast strategy, which includes a prototype-symmetric cross-entropy loss and a prototype-enhanced cross-entropy loss, to learn target intra-domain knowledge without relying on labels. A comprehensive variety of experiments on two domain adaptive semantic segmentation benchmarks demonstrates that the proposed end-to-end IAPC solution outperforms existing state-of-the-art methods. The source code is publicly available at https://***/yihong-97/Source-free-IAPC. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Quantum machine learning for multiclass classification beyond kernel methods

arXiv

引用

arXiv 2024年

作者： Ding, Chao Wang, Shi Wang, Yaonan Gao, Weibo College of Electrical and Information Engineering Hunan University Changsha410082 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Centre for Quantum Technologies National University of Singapore Singapore117543 Singapore The Photonics Institute Centre for Disruptive Photonic Technologies Nanyang Technological University Singapore637371 Singapore

Quantum machine learning is considered one of the current research fields with great potential. In recent years, Havlíček et al. [Nature 567, 209-212 (2019)] have proposed a quantum machine learning algorithm with quantum-enhanced feature spaces, which effectively addressed a binary classification problem on a superconducting processor and offered a potential pathway to achieving quantum advantage. However, a straightforward binary classification algorithm falls short in solving multiclass classification problems. In this paper, we propose a quantum algorithm that rigorously demonstrates that quantum kernel methods enhance the efficiency of multiclass classification in real-world applications, providing quantum advantage. To demonstrate quantum advantage, we design six distinct quantum kernels within the quantum algorithm to map input data into quantum state spaces and estimate the corresponding quantum kernel matrices. The results from quantum simulations reveal that the quantum algorithm outperforms its classical counterpart in handling six real-world multiclass classification problems. Furthermore, we leverage a variety of performance metrics to comprehensively evaluate the classification and generalization performance of the quantum algorithm. The results demonstrate that the quantum algorithm achieves superior classification and better generalization performance relative to classical counterparts. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum efficiency

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DANet: Density Adaptive Convolutional Network with Interactive Attention for 3D Point Clouds

arXiv

引用

arXiv 2023年

作者： He, Yong Yu, Hongshan Yang, Zhengeng Sun, Wei Feng, Mingtao Mian, Ajmal The National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University Lushan South Rd. Yuelu Dist. Changsha410082 China The School of Computer Science and Technology Xidian University Xi’an710071 China The Department of Computer Science The University of Western Australia PerthWA6009 Australia

Local features and contextual dependencies are crucial for 3D point cloud analysis. Many works have been devoted to designing better local convolutional kernels that exploit the contextual dependencies. However, current point convolutions lack robustness to varying point cloud density. Moreover, contextual modeling is dominated by non-local or self-attention models which are computationally expensive. To solve these problems, we propose density adaptive convolution, coined DAConv. The key idea is to adaptively learn the convolutional weights from geometric connections obtained from the point density and position. To extract precise context dependencies with fewer computations, we propose an interactive attention module (IAM) that embeds spatial information into channel attention along different spatial directions. DAConv and IAM are integrated in a hierarchical network architecture to achieve local density and contextual direction-aware learning for point cloud analysis. Experiments show that DAConv is significantly more robust to point density compared to existing methods and extensive comparisons on challenging 3D point cloud datasets show that our network achieves state-of-the-art classification results of 93.6% on ModelNet40, competitive semantic segmentation results of 68.71% mIoU on S3DIS and part segmentation results of 86.7% mIoU on ShapeNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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