arXiv subjects

Chi Huang

Publications and source records attributed to Chi Huang.

7 recordsLinked to original sources

arXiv2026

Stochastic Average Consensus Filtering and Distributed State Estimation for Boolean Control Networks

This paper addresses the distributed multi-sensor fusion state estimation and consensus filtering for Boolean control networks (BCNs). Existing centralized multi-sensor estimation schemes for stochastic BCNs have limitations of high communication costs and single-point failures, and continuous-state consensus algorithms are difficult to extend to discrete logical systems. By integrating probability measure transformation, semi-tensor product and stochastic approximation, a distributed stochastic average consensus filter is proposed. Moreover, the almost sure convergence of the algorithm is proved by martingale convergence theorem and perturbed stochastic Lyapunov functions. The proposed framework realizes global state estimation via local communication, avoiding the defects of centralized architectures.

eess.SY↗

arXiv2026

DrivingDepth: Sparse-Prompted Pixel-wise Scale Correction for Driving Depth Estimation

Dense depth estimation for autonomous driving faces a geometry-scale conflict: depth foundation models deliver pixel-aligned dense visual geometry without reliable metric scale, while projected LiDAR provides metric anchors that are sparse, noisy, and misaligned with image structures. Existing sparse-prompted methods incorporate LiDAR by regenerating depth from scratch, overriding the foundation model's coherent geometry and producing structural artifacts on visually continuous surfaces. Our key insight is that foundation models already capture geometrically coherent relative depth; no additional surface structure learning is required-only a per-pixel scale factor mapping relative geometry to metric coordinates. Based on this, we propose DrivingDepth, which treats sparse LiDAR as geometric prompts that locally calibrate a frozen foundation prior through residual pixel-wise scale correction, preserving dense visual geometry by construction. On nuScenes with 4-frame surround-view input, DrivingDepth achieves an AbsRel of 11.19 and an EdgeCR of 5.741, outperforming MapAnything (11.99/1.914) by simultaneously delivering SOTA metric accuracy and geometric consistency.

cs.CV↗

arXiv2025

FaithFusion: Harmonizing Reconstruction and Generation via Pixel-wise Information Gain

In controllable driving-scene reconstruction and 3D scene generation, maintaining geometric fidelity while synthesizing visually plausible appearance under large viewpoint shifts is crucial. However, effective fusion of geometry-based 3DGS and appearance-driven diffusion models faces inherent challenges, as the absence of pixel-wise, 3D-consistent editing criteria often leads to over-restoration and geometric drift. To address these issues, we introduce \textbf{FaithFusion}, a 3DGS-diffusion fusion framework driven by pixel-wise Expected Information Gain (EIG). EIG acts as a unified policy for coherent spatio-temporal synthesis: it guides diffusion as a spatial prior to refine high-uncertainty regions, while its pixel-level weighting distills the edits back into 3DGS. The resulting plug-and-play system is free from extra prior conditions and structural modifications.Extensive experiments on the Waymo dataset demonstrate that our approach attains SOTA performance across NTA-IoU, NTL-IoU, and FID, maintaining an FID of 107.47 even at 6 meters lane shift. Our code is available at https://github.com/wangyuanbiubiubiu/FaithFusion.

cs.CV↗

arXiv2024

NeRF-DetS: Enhanced Adaptive Spatial-wise Sampling and View-wise Fusion Strategies for NeRF-based Indoor Multi-view 3D Object Detection

In indoor scenes, the diverse distribution of object locations and scales makes the visual 3D perception task a big challenge. Previous works (e.g, NeRF-Det) have demonstrated that implicit representation has the capacity to benefit the visual 3D perception task in indoor scenes with high amount of overlap between input images. However, previous works cannot fully utilize the advancement of implicit representation because of fixed sampling and simple multi-view feature fusion. In this paper, inspired by sparse fashion method (e.g, DETR3D), we propose a simple yet effective method, NeRF-DetS, to address above issues. NeRF-DetS includes two modules: Progressive Adaptive Sampling Strategy (PASS) and Depth-Guided Simplified Multi-Head Attention Fusion (DS-MHA). Specifically, (1)PASS can automatically sample features of each layer within a dense 3D detector, using offsets predicted by the previous layer. (2)DS-MHA can not only efficiently fuse multi-view features with strong occlusion awareness but also reduce computational cost. Extensive experiments on ScanNetV2 dataset demonstrate our NeRF-DetS outperforms NeRF-Det, by achieving +5.02% and +5.92% improvement in mAP under IoU25 and IoU50, respectively. Also, NeRF-DetS shows consistent improvements on ARKITScenes.

cs.CV↗

arXiv2021

Does Head Label Help for Long-Tailed Multi-Label Text Classification

Multi-label text classification (MLTC) aims to annotate documents with the most relevant labels from a number of candidate labels. In real applications, the distribution of label frequency often exhibits a long tail, i.e., a few labels are associated with a large number of documents (a.k.a. head labels), while a large fraction of labels are associated with a small number of documents (a.k.a. tail labels). To address the challenge of insufficient training data on tail label classification, we propose a Head-to-Tail Network (HTTN) to transfer the meta-knowledge from the data-rich head labels to data-poor tail labels. The meta-knowledge is the mapping from few-shot network parameters to many-shot network parameters, which aims to promote the generalizability of tail classifiers. Extensive experimental results on three benchmark datasets demonstrate that HTTN consistently outperforms the state-of-the-art methods. The code and hyper-parameter settings are released for reproducibility

cs.CL↗

arXiv2019

The Mass-Metallicity Relation at $z\sim0.8$: Redshift Evolution and Parameter Dependency

The spectra of emission-line galaxies (ELGs) from the extended Baryon Oscillation Spectroscopic Survey (eBOSS) of the Sloan Digit Sky Survey (SDSS) are used to study the mass-metallicity relation (MZR) at $z\sim0.8$. The selected sample contains about 180,000 massive star-forming galaxies with $0.6 < z < 1.05$ and $9 < {\rm log}(M_{\star}/M_{\odot}) < 12$. The spectra are stacked in bins of different parameters including redshift, stellar mass, star formation rate (SFR), specific star formation rate (sSFR), half-light radius, mass density, and optical color. The average MZR at $z\sim0.83$ has a downward evolution in the MZR from local to high-redshift universe, which is consistent with previous works. At a specified stellar mass, galaxies with higher SFR/sSFR and larger half-light radius have systematically lower metallicity. This behavior is reversed for galaxies with larger mass density and optical color. Among the above physical parameters, the MZR has the most significant dependency on SFR. Our galaxy sample at $0.6<z<1.05$ approximately follows the fundamental metallicity relation (FMR) in the local universe, although the sample inhomogeneity and incompleteness might have effect on our MZR and FMR.

astro-ph.GA↗

arXiv2018

Mass - metallicity relation and fundamental metallicity relation of metal-poor star-forming galaxies at $0.6<z<0.9$ from the eBOSS survey

The stellar mass-metallicity relation ($M_* - Z$, MZR) indicates that the metallicities of galaxies increase with increasing stellar masses. The fundamental metallicity relation (FMR) suggests that the galaxies with higher star formation rates (SFRs) tend to have lower metallicities for a given stellar mass. To examine whether the MZR and FMR still hold at poorer metallicities and higher redshifts, we compile a sample of 35 star-forming galaxies (SFGs) at $0.6<z<0.9$ using the public spectral database ($\rm v5\_10\_0$) of emission-line galaxies from the extended Baryon Oscillation Spectroscopic Survey (eBOSS). These galaxies are identified for their significant auroral $\rm [OIII]\lambda4363$ emission line ($\rm S/N \geq 3$). With the electronic temperature metallicity calibration, we find nine SFGs are extremely metal-poor galaxies with $\rm 12 + log(O/H) \leq 7.69 \ (1/10 \ Z_\odot)$. The metallicity of the most metal-deficient galaxy is $7.35\pm 0.09$ (about 1/20 $Z_{\odot}$). Compared with the SFGs with normal metallicities in local and high redshift universe, our metal-poor SFGs have more than ten times higher SFRs at a fixed stellar mass. We create a new mass -- SFR relation for these metal-poor galaxies at $0.6<z<0.9$. Due to the higher SFRs and younger stellar ages, our metal-poor SFGs deviate from the MZR and FMR in the local universe toward lower metallicities, confirming the existence of FMR, as well as the cosmic evolution of MZR and FMR with redshift.

astro-ph.GA↗