arXiv subjects

Chen Long

Publications and source records attributed to Chen Long.

9 recordsLinked to original sources

arXiv2026

The properties and predictions of quasi-periodic oscillations around a black hole in nonlocal gravity

We investigate the dynamics of massive test particles around a static black hole in nonlocal gravity and examine the corresponding properties of HF QPOs, constraining the nonlocal parameter to $\alpha/M \leq 0.452$. The nonlocal parameter $\alpha$ enhances the effective potential $V_{eff}$ and leads to a systematic reduction in the energy E and angular momentum L of circular orbits. Consequently, the ISCO radius, along with the associated energy and angular momentum, decreases monotonically with $\alpha$, while the radiative efficiency increases, reaching a maximum of approximately $8.9\%$. Due to the spherical symmetry of the spacetime, the Keplerian frequency $\Omega_{\phi}$ and the vertical epicyclic frequency $\Omega_{\theta}$ coincide and are suppressed by $\alpha$, whereas the radial epicyclic frequency $\Omega_{r}$ is enhanced. The impact of $\alpha$ on several twin-peak HF QPO models is examined, revealing that $\alpha$ increases both the lower and upper bounds of the predicted QPO frequency ranges. By imposing the $2\nu_U = 3\nu_L$ resonance condition, we analyze the resonant radius, upper QPO frequency, maximum allowed black hole mass, and the time delay between the shadow and QPO signals. We find that the resonant radius decreases with $\alpha$, while the upper QPO frequency increases, spanning the range $\nu_U \sim(673/M-4360/M)$Hz. When the TOV imit is imposed, the upper frequency is further constrained to $\nu_U \lesssim 1450$Hz. Combining astronomical observations for the classification of QPOs, where $\nu_U \geq 100$Hz, the black hole mass in the nonlocal gravity should satisfy $M \lesssim 43.6M_\odot$. Although the radial separation between the resonant radius and the photon sphere decreases with $\alpha$, the associated gravitational time delay increases, remaining below $\sim 1.3$ms and thus negligible for current observational capabilities.

gr-qc↗

arXiv2026

Style-Decoupled Adaptive Routing Network for Underwater Image Enhancement

Underwater Image Enhancement (UIE) is essential for robust visual perception in marine applications. However, existing methods predominantly rely on uniform mapping tailored to average dataset distributions, leading to over-processing mildly degraded images or insufficient recovery for severe ones. To address this challenge, we propose a novel adaptive enhancement framework, SDAR-Net. Unlike existing uniform paradigms, it first decouples specific degradation styles from the input and subsequently modulates the enhancement process adaptively. Specifically, since underwater degradation primarily shifts the appearance while keeping the scene structure, SDAR-Net formulates image features into dynamic degradation style embeddings and static scene structural representations through a carefully designed training framework. Subsequently, we introduce an adaptive routing mechanism. By evaluating style features and adaptively predicting soft weights at different enhancement states, it guides the weighted fusion of the corresponding image representations, accurately satisfying the adaptive restoration demands of each image. Extensive experiments show that SDAR-Net achieves a new state-of-the-art (SOTA) performance with a PSNR of 25.72 dB on real-world benchmark, and demonstrates its utility in downstream vision tasks. Our code is available at https://github.com/WHU-USI3DV/SDAR-Net.

cs.CV↗

arXiv2026

Expert Knowledge-Guided Decision Calibration for Accurate Fine-Grained Tree Species Classification

Accurate fine-grained tree species classification is critical for forest inventory and biodiversity monitoring. Existing methods predominantly focus on designing complex architectures to fit local data distributions. However, they often overlook the long-tailed distributions and high inter-class similarity inherent in limited data, thereby struggling to distinguish between few-shot or confusing categories. In the process of knowledge dissemination in the human world, individuals will actively seek expert assistance to transcend the limitations of local thinking. Inspired by this, we introduce an external "Domain Expert" and propose an Expert Knowledge-Guided Classification Decision Calibration Network (EKDC-Net) to overcome these challenges. Our framework addresses two core issues: expert knowledge extraction and utilization. Specifically, we first develop a Local Prior Guided Knowledge Extraction Module (LPKEM). By leveraging Class Activation Map (CAM) analysis, LPKEM guides the domain expert to focus exclusively on discriminative features essential for classification. Subsequently, to effectively integrate this knowledge, we design an Uncertainty-Guided Decision Calibration Module (UDCM). This module dynamically corrects the local model's decisions by considering both overall category uncertainty and instance-level prediction uncertainty. Furthermore, we present a large-scale classification dataset covering 102 tree species, named CU-Tree102 to address the issue of scarce diversity in current benchmarks. Experiments on three benchmark datasets demonstrate that our approach achieves state-of-the-art performance. Crucially, as a lightweight plug-and-play module, EKDC-Net improves backbone accuracy by 6.42% and precision by 11.46% using only 0.08M additional learnable parameters. The dataset, code, and pre-trained models are available at https://github.com/WHU-USI3DV/TreeCLS.

cs.CV↗

arXiv2025

WHU-STree: A Multi-modal Benchmark Dataset for Street Tree Inventory

Street trees are vital to urban livability, providing ecological and social benefits. Establishing a detailed, accurate, and dynamically updated street tree inventory has become essential for optimizing these multifunctional assets within space-constrained urban environments. Given that traditional field surveys are time-consuming and labor-intensive, automated surveys utilizing Mobile Mapping Systems (MMS) offer a more efficient solution. However, existing MMS-acquired tree datasets are limited by small-scale scene, limited annotation, or single modality, restricting their utility for comprehensive analysis. To address these limitations, we introduce WHU-STree, a cross-city, richly annotated, and multi-modal urban street tree dataset. Collected across two distinct cities, WHU-STree integrates synchronized point clouds and high-resolution images, encompassing 21,007 annotated tree instances across 50 species and 2 morphological parameters. Leveraging the unique characteristics, WHU-STree concurrently supports over 10 tasks related to street tree inventory. We benchmark representative baselines for two key tasks--tree species classification and individual tree segmentation. Extensive experiments and in-depth analysis demonstrate the significant potential of multi-modal data fusion and underscore cross-domain applicability as a critical prerequisite for practical algorithm deployment. In particular, we identify key challenges and outline potential future works for fully exploiting WHU-STree, encompassing multi-modal fusion, multi-task collaboration, cross-domain generalization, spatial pattern learning, and Multi-modal Large Language Model for street tree asset management. The WHU-STree dataset is accessible at: https://github.com/WHU-USI3DV/WHU-STree.

cs.CV↗

arXiv2024

Explicitly Guided Information Interaction Network for Cross-modal Point Cloud Completion

In this paper, we explore a novel framework, EGIInet (Explicitly Guided Information Interaction Network), a model for View-guided Point cloud Completion (ViPC) task, which aims to restore a complete point cloud from a partial one with a single view image. In comparison with previous methods that relied on the global semantics of input images, EGIInet efficiently combines the information from two modalities by leveraging the geometric nature of the completion task. Specifically, we propose an explicitly guided information interaction strategy supported by modal alignment for point cloud completion. First, in contrast to previous methods which simply use 2D and 3D backbones to encode features respectively, we unified the encoding process to promote modal alignment. Second, we propose a novel explicitly guided information interaction strategy that could help the network identify critical information within images, thus achieving better guidance for completion. Extensive experiments demonstrate the effectiveness of our framework, and we achieved a new state-of-the-art (+16% CD over XMFnet) in benchmark datasets despite using fewer parameters than the previous methods. The pre-trained model and code and are available at https://github.com/WHU-USI3DV/EGIInet.

cs.CV↗

arXiv2024

WHU-Synthetic: A Synthetic Perception Dataset for 3-D Multitask Model Research

End-to-end models capable of handling multiple sub-tasks in parallel have become a new trend, thereby presenting significant challenges and opportunities for the integration of multiple tasks within the domain of 3D vision. The limitations of 3D data acquisition conditions have not only restricted the exploration of many innovative research problems but have also caused existing 3D datasets to predominantly focus on single tasks. This has resulted in a lack of systematic approaches and theoretical frameworks for 3D multi-task learning, with most efforts merely serving as auxiliary support to the primary task. In this paper, we introduce WHU-Synthetic, a large-scale 3D synthetic perception dataset designed for multi-task learning, from the initial data augmentation (upsampling and depth completion), through scene understanding (segmentation), to macro-level tasks (place recognition and 3D reconstruction). Collected in the same environmental domain, we ensure inherent alignment across sub-tasks to construct multi-task models without separate training methods. Besides, we implement several novel settings, making it possible to realize certain ideas that are difficult to achieve in real-world scenarios. This supports more adaptive and robust multi-task perception tasks, such as sampling on city-level models, providing point clouds with different densities, and simulating temporal changes. Using our dataset, we conduct several experiments to investigate mutual benefits between sub-tasks, revealing new observations, challenges, and opportunities for future research. The dataset is accessible at https://github.com/WHU-USI3DV/WHU-Synthetic.

cs.CV↗

arXiv2023

SparseDC: Depth Completion from sparse and non-uniform inputs

We propose SparseDC, a model for Depth Completion of Sparse and non-uniform depth inputs. Unlike previous methods focusing on completing fixed distributions on benchmark datasets (e.g., NYU with 500 points, KITTI with 64 lines), SparseDC is specifically designed to handle depth maps with poor quality in real usage. The key contributions of SparseDC are two-fold. First, we design a simple strategy, called SFFM, to improve the robustness under sparse input by explicitly filling the unstable depth features with stable image features. Second, we propose a two-branch feature embedder to predict both the precise local geometry of regions with available depth values and accurate structures in regions with no depth. The key of the embedder is an uncertainty-based fusion module called UFFM to balance the local and long-term information extracted by CNNs and ViTs. Extensive indoor and outdoor experiments demonstrate the robustness of our framework when facing sparse and non-uniform input depths. The pre-trained model and code are available at https://github.com/WHU-USI3DV/SparseDC.

cs.CV↗

arXiv2021

PC$^2$-PU: Patch Correlation and Point Correlation for Effective Point Cloud Upsampling

Point cloud upsampling is to densify a sparse point set acquired from 3D sensors, providing a denser representation for the underlying surface. Existing methods divide the input points into small patches and upsample each patch separately, however, ignoring the global spatial consistency between patches. In this paper, we present a novel method PC$^2$-PU, which explores patch-to-patch and point-to-point correlations for more effective and robust point cloud upsampling. Specifically, our network has two appealing designs: (i) We take adjacent patches as supplementary inputs to compensate the loss structure information within a single patch and introduce a Patch Correlation Module to capture the difference and similarity between patches. (ii) After augmenting each patch's geometry, we further introduce a Point Correlation Module to reveal the relationship of points inside each patch to maintain the local spatial consistency. Extensive experiments on both synthetic and real scanned datasets demonstrate that our method surpasses previous upsampling methods, particularly with the noisy inputs. The code and data are at \url{https://github.com/chenlongwhu/PC2-PU.git}.

cs.CV↗

arXiv2018

Observation of extremely long exciton lifetime in Janus-MoSTe monolayer

The electron-hole separation efficiency is a key factor that determines the performance of two-dimensional (2D) transition metal dichalcogenides (TMDs) and devices. Therefore, searching for novel 2D TMD materials with long timescale of carrier lifetime becomes one of the most important topics. Here, based on the timedomain density functional theory (TD-DFT), we propose a brand new TMD material, namely janus-MoSTe, which exhibits a strong build-in electric field. Our results show that in janus-MoSTe monolayer, the exciton consisting of electron and hole has a relatively wide spatial extension and low binding energy. In addition, a slow electron-hole recombination process is observed, with timescale on the order of 1.31 ns, which is even comparable with those of van der Waals (vdW) heterostructures. Further analysis reveals that the extremely long timescale for electron-hole recombination could be ascribed to the strong Coulomb screening effect as well as the small overlap of wavefunctions between electrons and holes. Our findings establish the build-in electric field as an effective factor to control the electron-hole recombination dynamics in TMD monolayers and facilitate their future applications in light detecting and harvesting.

cond-mat.mtrl-sci↗