arXiv subjects

Chao Xu

Publications and source records attributed to Chao Xu.

At least 145 records · Page 8Linked to original sources

arXiv2025

SEB-Naver: A SE(2)-based Local Navigation Framework for Car-like Robots on Uneven Terrain

Autonomous navigation of car-like robots on uneven terrain poses unique challenges compared to flat terrain, particularly in traversability assessment and terrain-associated kinematic modelling for motion planning. This paper introduces SEB-Naver, a novel SE(2)-based local navigation framework designed to overcome these challenges. First, we propose an efficient traversability assessment method for SE(2) grids, leveraging GPU parallel computing to enable real-time updates and maintenance of local maps. Second, inspired by differential flatness, we present an optimization-based trajectory planning method that integrates terrain-associated kinematic models, significantly improving both planning efficiency and trajectory quality. Finally, we unify these components into SEB-Naver, achieving real-time terrain assessment and trajectory optimization. Extensive simulations and real-world experiments demonstrate the effectiveness and efficiency of our approach. The code is at https://github.com/ZJU-FAST-Lab/seb_naver.

cs.RO↗

arXiv2025

Towards Quantum SAGINs Harnessing Optical RISs: Applications, Advances, and the Road Ahead

The space-air-ground integrated network (SAGIN) concept is vital for the development of seamless next-generation (NG) wireless coverage, integrating satellites, unmanned aerial vehicles, and manned aircraft along with the terrestrial infrastructure to provide resilient ubiquitous communications. By incorporating quantum communications using optical wireless signals, SAGIN is expected to support a synergistic global quantum Internet alongside classical networks. However, long-distance optical beam propagation requires line-of-sight (LoS) connections in the face of beam broadening and LoS blockages. To overcome blockages among SAGIN nodes, we propose deploying optical reconfigurable intelligent surfaces (ORISs) on building rooftops. They can also adaptively control optical beam diameters for reducing losses. This article first introduces the applications of ORISs in SAGINs, then examines their advances in quantum communications for typical SAGIN scenarios. Finally, the road ahead towards the practical realization of ORIS-aided NG quantum SAGINs is outlined.

eess.SP↗

arXiv2025

GPT4Image: Large Pre-trained Models Help Vision Models Learn Better on Perception Task

The upsurge in pre-trained large models started by ChatGPT has swept across the entire deep learning community. Such powerful models demonstrate advanced generative ability and multimodal understanding capability, which quickly set new state of the arts on a variety of benchmarks. The pre-trained LLM usually plays the role as a universal AI model that can conduct various tasks like article analysis and image comprehension. However, due to the prohibitively high memory and computational cost of implementing such a large model, the conventional models (such as CNN and ViT) are still essential for many visual perception tasks. In this paper, we propose to enhance the representation ability of ordinary vision models on perception tasks (e.g. image classification) by taking advantage of the off-the-shelf large pre-trained models. We present a new learning framework, dubbed GPT4Image, where the knowledge of the large pre-trained models are extracted to help CNNs and ViTs learn better representations and achieve higher performance. Firstly, we curate a high quality description set by prompting a multimodal LLM to generate descriptions for training images. Then, these detailed descriptions are fed into a pre-trained encoder to extract text embeddings that encodes the rich semantics of images. During training, text embeddings will serve as extra supervising signal and be aligned with image representations learned by vision models. The alignment process helps vision models achieve better performance with the aid of pre-trained LLMs. We conduct extensive experiments to verify the effectiveness of the proposed algorithm on various visual perception tasks for heterogeneous model architectures.

cs.CV↗

arXiv2025

Primitive-Swarm: An Ultra-lightweight and Scalable Planner for Large-scale Aerial Swarms

Achieving large-scale aerial swarms is challenging due to the inherent contradictions in balancing computational efficiency and scalability. This paper introduces Primitive-Swarm, an ultra-lightweight and scalable planner designed specifically for large-scale autonomous aerial swarms. The proposed approach adopts a decentralized and asynchronous replanning strategy. Within it is a novel motion primitive library consisting of time-optimal and dynamically feasible trajectories. They are generated utlizing a novel time-optimial path parameterization algorithm based on reachability analysis (TOPP-RA). Then, a rapid collision checking mechanism is developed by associating the motion primitives with the discrete surrounding space according to conflicts. By considering both spatial and temporal conflicts, the mechanism handles robot-obstacle and robot-robot collisions simultaneously. Then, during a replanning process, each robot selects the safe and minimum cost trajectory from the library based on user-defined requirements. Both the time-optimal motion primitive library and the occupancy information are computed offline, turning a time-consuming optimization problem into a linear-complexity selection problem. This enables the planner to comprehensively explore the non-convex, discontinuous 3-D safe space filled with numerous obstacles and robots, effectively identifying the best hidden path. Benchmark comparisons demonstrate that our method achieves the shortest flight time and traveled distance with a computation time of less than 1 ms in dense environments. Super large-scale swarm simulations, involving up to 1000 robots, running in real-time, verify the scalability of our method. Real-world experiments validate the feasibility and robustness of our approach. The code will be released to foster community collaboration.

cs.RO↗

arXiv2025

Stacked Intelligent Metasurface-Based Transceiver Design for Near-Field Wideband Systems

Intelligent metasurfaces may be harnessed for realizing efficient holographic multiple-input and multiple-output (MIMO) systems, at a low hardware-cost and high energy-efficiency. As part of this family, we propose a hybrid beamforming design for stacked intelligent metasurfaces (SIM) aided wideband wireless systems relying on the near-field channel model. Specifically, the holographic beamformer is designed based on configuring the phase shifts in each layer of the SIM for maximizing the sum of the baseband eigen-channel gains of all users. To optimize the SIM phase shifts, we propose a layer-by-layer iterative algorithm for optimizing the phase shifts in each layer alternately. Then, the minimum mean square error (MMSE) transmit precoding method is employed for the digital beamformer to support multi-user access. Furthermore, the mitigation of the SIM phase tuning error is also taken into account in the digital beamformer by exploiting its statistics. The power sharing ratio of each user is designed based on the iterative waterfilling power allocation algorithm. Additionally, our analytical results indicate that the spectral efficiency attained saturates in the high signal-to-noise ratio (SNR) region due to the phase tuning error resulting from the imperfect SIM hardware quality. The simulation results show that the SIM-aided holographic MIMO outperforms the state-of-the-art (SoA) single-layer holographic MIMO in terms of its achievable rate. We further demonstrate that the near-field channel model allows the SIM-based transceiver design to support multiple users, since the spatial resources represented both by the angle domain and the distance domain can be exploited.

cs.IT↗

arXiv2025

Safe and Agile Transportation of Cable-Suspended Payload via Multiple Aerial Robots

Transporting a heavy payload using multiple aerial robots (MARs) is an efficient manner to extend the load capacity of a single aerial robot. However, existing schemes for the multiple aerial robots transportation system (MARTS) still lack the capability to generate a collision-free and dynamically feasible trajectory in real-time and further track an agile trajectory especially when there are no sensors available to measure the states of payload and cable. Therefore, they are limited to low-agility transportation in simple environments. To bridge the gap, we propose complete planning and control schemes for the MARTS, achieving safe and agile aerial transportation (SAAT) of a cable-suspended payload in complex environments. Flatness maps for the aerial robot considering the complete kinematical constraint and the dynamical coupling between each aerial robot and payload are derived. To improve the responsiveness for the generation of the safe, dynamically feasible, and agile trajectory in complex environments, a real-time spatio-temporal trajectory planning scheme is proposed for the MARTS. Besides, we break away from the reliance on the state measurement for both the payload and cable, as well as the closed-loop control for the payload, and propose a fully distributed control scheme to track the agile trajectory that is robust against imprecise payload mass and non-point mass payload. The proposed schemes are extensively validated through benchmark comparisons, ablation studies, and simulations. Finally, extensive real-world experiments are conducted on a MARTS integrated by three aerial robots with onboard computers and sensors. The result validates the efficiency and robustness of our proposed schemes for SAAT in complex environments.

cs.RO↗

arXiv2025

CDMA/OTFS Sensing Outperforms Pure OTFS at the Same Communication Throughput

There is a dearth of publications on the subject of spreading-aided Orthogonal Time Frequency Space (OTFS) solutions, especially for Integrated Sensing and Communication (ISAC), even though Code Division Multiple Access (CDMA) assisted multi-user OTFS (CDMA/OTFS) exhibits tangible benefits. Hence, this work characterises both the communication Bit Error Rate (BER) and sensing Root Mean Square Error (RMSE) performance of CDMA/OTFS, and contrasts them to pure OTFS. Three CDMA/OTFS configurations are considered: Delay Code Division Multiple Access OTFS (Dl-CDMA/OTFS), Doppler Code Division Multiple Access OTFS (Dp-CDMA/OTFS), and Delay Doppler Code Division Multiple Access OTFS (DD-CDMA/OTFS), which harness direct sequence spreading along the delay axis, Doppler axis, and DD domains respectively. For each configuration, the performance of Gold, Hadamard, and Zadoff-Chu sequences is investigated. The results demonstrate that Zadoff-Chu Dl-CDMA/OTFS and DD-CDMA/OTFS consistently outperform pure OTFS sensing, whilst maintaining a similar communication performance at the same throughput. The extra modulation complexity of CDMA/OTFS is similar to that of other OTFS multi-user methodologies, but the demodulation complexity of CDMA/OTFS is lower than that of some other OTFS multi-user methodologies. CDMA/OTFS sensing can also consistently outperform OTFS sensing whilst not requiring any additional complexity for target parameter estimation. Therefore, CDMA/OTFS is an appealing candidate for implementing multi-user OTFS ISAC.

eess.SP↗

arXiv2025

Holographic Metasurface-Based Beamforming for Multi-Altitude LEO Satellite Networks

Low Earth Orbit (LEO) satellite networks are capable of improving the global Internet service coverage. In this context, we propose a hybrid beamforming design for holographic metasurface based terrestrial users in multi-altitude LEO satellite networks. Firstly, the holographic beamformer is optimized by maximizing the downlink channel gain from the serving satellite to the terrestrial user. Then, the digital beamformer is designed by conceiving a minimum mean square error (MMSE) based detection algorithm for mitigating the interference arriving from other satellites. To dispense with excessive overhead of full channel state information (CSI) acquisition of all satellites, we propose a low-complexity MMSE beamforming algorithm that only relies on the distribution of the LEO satellite constellation harnessing stochastic geometry, which can achieve comparable throughput to that of the algorithm based on the full CSI in the case of a dense LEO satellite deployment. Furthermore, it outperforms the maximum ratio combining (MRC) algorithm, thanks to its inter-satellite interference mitigation capacity. The simulation results show that our proposed holographic metasurface based hybrid beamforming architecture is capable of outperforming the state-of-the-art antenna array architecture in terms of its throughput, given the same physical size of the transceivers. Moreover, we demonstrate that the beamforming performance attained can be substantially improved by taking into account the mutual coupling effect, imposed by the dense placement of the holographic metasurface elements.

cs.IT↗

arXiv2024

Space-Air-Ground Integrated Networks: Their Channel Model and Performance Analysis

Given their extensive geographic coverage, low Earth orbit (LEO) satellites are envisioned to find their way into next-generation (6G) wireless communications. This paper explores space-air-ground integrated networks (SAGINs) leveraging LEOs to support terrestrial and non-terrestrial users. We first propose a practical satellite-ground channel model that incorporates five key aspects: 1) the small-scale fading characterized by the Shadowed-Rician distribution in terms of the Rician factor K, 2) the path loss effect of bending rays due to atmospheric refraction, 3) the molecular absorption modelled by the Beer-Lambert law, 4) the Doppler effects including the Earth's rotation, and 5) the impact of weather conditions according to the International Telecommunication Union Recommendations (ITU-R). Harnessing the proposed model, we analyze the long-term performance of the SAGIN considered. Explicitly, the closed-form expressions of both the outage probability and of the ergodic rates are derived. Additionally, the upper bounds of bit-error rates and of the Goodput are investigated. The numerical results yield the following insights: 1) The shadowing effect and the ratio between the line-of-sight and scattering components can be conveniently modeled by the factors of K and m in the proposed Shadowed-Rician small-scale fading model. 2) The atmospheric refraction has a modest effect on the path loss. 3) When calculating the transmission distance of waves, Earth's curvature and its geometric relationship with the satellites must be considered, particularly at small elevation angles. 3) High-frequency carriers suffer from substantial path loss, and 4) the Goodput metric is eminently suitable for characterizing the performance of different coding as well as modulation methods and of the estimation error of the Doppler effects.

cs.IT↗

arXiv2024

Optical RISs Improve the Secret Key Rate of Free-Space QKD in HAP-to-UAV Scenarios

Large optical reconfigurable intelligent surfaces (ORISs) are proposed for employment on building rooftops to facilitate free-space quantum key distribution (QKD) between highaltitude platforms (HAPs) and low-altitude platforms (LAPs). Due to practical constraints, the communication terminals can only be positioned beneath the LAPs, preventing direct upward links to HAPs. By deploying ORISs on rooftops to reflect the beam arriving from HAPs towards LAPs from below, reliable HAP-to-LAP links can be established. To accurately characterize the optical beam propagation, we develop an analytical channel model based on extended Huygens-Fresnel principles for representing both the atmospheric turbulence effects and the hovering fluctuations of LAPs. This model facilitates adaptive ORIS beam-width control through linear, quadratic, and focusing phase shifts, which are capable of effectively mitigating the detrimental effects of beam broadening and pointing errors (PE). Consequently, the information-theoretic bound of the secret key rate and the security performance of a decoy-state QKD protocol are analyzed. Our findings demonstrate that quadratic phase shifts enhance the SKR at high HAP-ORIS zenith angles or mild PE conditions by narrowing the beam to optimal sizes. By contrast, linear phase shifts are advantageous at low HAP-ORIS zenith angles or moderate-to-high PE by diverging the beam to mitigate LAP fluctuations.

eess.SP↗

arXiv2024

MCMat: Multiview-Consistent and Physically Accurate PBR Material Generation

Existing 2D methods utilize UNet-based diffusion models to generate multi-view physically-based rendering (PBR) maps but struggle with multi-view inconsistency, while some 3D methods directly generate UV maps, encountering generalization issues due to the limited 3D data. To address these problems, we propose a two-stage approach, including multi-view generation and UV materials refinement. In the generation stage, we adopt a Diffusion Transformer (DiT) model to generate PBR materials, where both the specially designed multi-branch DiT and reference-based DiT blocks adopt a global attention mechanism to promote feature interaction and fusion between different views, thereby improving multi-view consistency. In addition, we adopt a PBR-based diffusion loss to ensure that the generated materials align with realistic physical principles. In the refinement stage, we propose a material-refined DiT that performs inpainting in empty areas and enhances details in UV space. Except for the normal condition, this refinement also takes the material map from the generation stage as an additional condition to reduce the learning difficulty and improve generalization. Extensive experiments show that our method achieves state-of-the-art performance in texturing 3D objects with PBR materials and provides significant advantages for graphics relighting applications. Project Page: https://lingtengqiu.github.io/2024/MCMat/

cs.CV↗

arXiv2024

Instruct-IPT: All-in-One Image Processing Transformer via Weight Modulation

Due to the unaffordable size and intensive computation costs of low-level vision models, All-in-One models that are designed to address a handful of low-level vision tasks simultaneously have been popular. However, existing All-in-One models are limited in terms of the range of tasks and performance. To overcome these limitations, we propose Instruct-IPT -- an All-in-One Image Processing Transformer (IPT) that could effectively address manifold image restoration tasks with large inter-task gaps, such as denoising, deblurring, deraining, dehazing, and desnowing. While most research propose feature adaptation methods, we reveal their failure in addressing highly distinct tasks, and suggest weight modulation that adapts weights to specific tasks. Firstly, we search for task-sensitive weights and introduce task-specific biases on top of them. Secondly, we conduct rank analysis for a good compression strategy and perform low-rank decomposition on the biases. Thirdly, we propose synchronous training that updates the task-general backbone model and the task-specific biases simultaneously. In this way, the model is instructed to learn both general and task-specific knowledge. Via our simple yet effective method that instructs the IPT to be task experts, Instruct-IPT could better cooperate between tasks with distinct characteristics at humble costs. As an additional feature, we enable Instruct-IPT to receive human prompts. We have conducted experiments on Instruct-IPT to demonstrate the effectiveness of our method on manifold tasks, and we have effectively extended our method to diffusion denoisers as well. The code is available at https://github.com/huawei-noah/Pretrained-IPT.

cs.CV↗

arXiv2024

AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction

Generating animatable human avatars from a single image is essential for various digital human modeling applications. Existing 3D reconstruction methods often struggle to capture fine details in animatable models, while generative approaches for controllable animation, though avoiding explicit 3D modeling, suffer from viewpoint inconsistencies in extreme poses and computational inefficiencies. In this paper, we address these challenges by leveraging the power of generative models to produce detailed multi-view canonical pose images, which help resolve ambiguities in animatable human reconstruction. We then propose a robust method for 3D reconstruction of inconsistent images, enabling real-time rendering during inference. Specifically, we adapt a transformer-based video generation model to generate multi-view canonical pose images and normal maps, pretraining on a large-scale video dataset to improve generalization. To handle view inconsistencies, we recast the reconstruction problem as a 4D task and introduce an efficient 3D modeling approach using 4D Gaussian Splatting. Experiments demonstrate that our method achieves photorealistic, real-time animation of 3D human avatars from in-the-wild images, showcasing its effectiveness and generalization capability.

cs.CV↗

arXiv2024

A Spectral Element Enrichment Wall-Model

We develop an enrichment wall-model within the spectral element method (SEM) framework for large-eddy simulations of wall-bounded flows. The method augments the polynomial solution in the wall-adjacent elements with an analytical law-of-the-wall enrichment function representing the mean velocity near the wall. In the solution representation, this enrichment function captures the large gradients in the boundary layer, which allows the polynomial modes to represent the turbulent fluctuations. The enriched solution is able to resolve the shear stress at the wall without any modification to the no-slip wall boundary conditions. The performance of the SEM enrichment wall-modeling approach is assessed in turbulent channel flow LES for a range of Reynolds numbers. The results show that the proposed method improves solution accuracy on under-resolved near-wall grids as compared to the traditional shear stress wall-models.

physics.flu-dyn↗

arXiv2024

Theoretically Guaranteed Distribution Adaptable Learning

In many open environment applications, data are collected in the form of a stream, which exhibits an evolving distribution over time. How to design algorithms to track these evolving data distributions with provable guarantees, particularly in terms of the generalization ability, remains a formidable challenge. To handle this crucial but rarely studied problem and take a further step toward robust artificial intelligence, we propose a novel framework called Distribution Adaptable Learning (DAL). It enables the model to effectively track the evolving data distributions. By Encoding Feature Marginal Distribution Information (EFMDI), we broke the limitations of optimal transport to characterize the environmental changes and enable model reuse across diverse data distributions. It can enhance the reusable and evolvable properties of DAL in accommodating evolving distributions. Furthermore, to obtain the model interpretability, we not only analyze the generalization error bound of the local step in the evolution process, but also investigate the generalization error bound associated with the entire classifier trajectory of the evolution based on the Fisher-Rao distance. For demonstration, we also present two special cases within the framework, together with their optimizations and convergence analyses. Experimental results over both synthetic and real-world data distribution evolving tasks validate the effectiveness and practical utility of the proposed framework.

cs.LG↗

arXiv2024

Precoded faster-than-Nyquist signaling using optimal power allocation for OTFS

A precoded orthogonal time frequency space (OTFS) modulation scheme relying on faster-than-Nyquist (FTN) transmission over doubly selective fading channels is {proposed}, which enhances the spectral efficiency and improves the Doppler resilience. We derive the input-output relationship of the FTN signaling in the delay-Doppler domain. Eigenvalue decomposition (EVD) is used for eliminating both the effects of inter-symbol interference and correlated additive noise encountered in the delay-Doppler domain to enable efficient symbol-by-symbol demodulation. Furthermore, the power allocation coefficients of individual frames are optimized for maximizing the mutual information under the constraint of the derived total transmit power. Our performance results demonstrate that the proposed FTN-based OTFS scheme can enhance the information rate while achieving a comparable BER performance to that of its conventional Nyquist-based OTFS counterpart that employs the same root-raised-cosine shaping filter.

eess.SP↗

arXiv2024

Task-oriented Age of Information for Remote Monitoring Systems

The emergence of intelligent applications has fostered the development of a task-oriented communication paradigm, where a comprehensive, universal, and practical metric is crucial for unleashing the potential of this paradigm. To this end, we introduce an innovative metric, the Task-oriented Age of Information (TAoI), to measure whether the content of information is relevant to the system task, thereby assisting the system in efficiently completing designated tasks. Also, we study the TAoI in a remote monitoring system, whose task is to identify target images and transmit them for subsequent analysis. We formulate the dynamic transmission problem as a Semi-Markov Decision Process (SMDP) and transform it into an equivalent Markov Decision Process (MDP) to minimize TAoI and find the optimal transmission policy. Furthermore, we demonstrate that the optimal strategy is a threshold-based policy regarding TAoI and propose a relative value iteration algorithm based on the threshold structure to obtain the optimal transmission policy. Finally, simulation results show the superior performance of the optimal transmission policy compared to the baseline policies.

cs.NI↗

arXiv2024

Stereo-Talker: Audio-driven 3D Human Synthesis with Prior-Guided Mixture-of-Experts

This paper introduces Stereo-Talker, a novel one-shot audio-driven human video synthesis system that generates 3D talking videos with precise lip synchronization, expressive body gestures, temporally consistent photo-realistic quality, and continuous viewpoint control. The process follows a two-stage approach. In the first stage, the system maps audio input to high-fidelity motion sequences, encompassing upper-body gestures and facial expressions. To enrich motion diversity and authenticity, large language model (LLM) priors are integrated with text-aligned semantic audio features, leveraging LLMs' cross-modal generalization power to enhance motion quality. In the second stage, we improve diffusion-based video generation models by incorporating a prior-guided Mixture-of-Experts (MoE) mechanism: a view-guided MoE focuses on view-specific attributes, while a mask-guided MoE enhances region-based rendering stability. Additionally, a mask prediction module is devised to derive human masks from motion data, enhancing the stability and accuracy of masks and enabling mask guiding during inference. We also introduce a comprehensive human video dataset with 2,203 identities, covering diverse body gestures and detailed annotations, facilitating broad generalization. The code, data, and pre-trained models will be released for research purposes.

cs.CV↗