arXiv subjects

Xinming Huang

Publications and source records attributed to Xinming Huang.

At least 19 recordsLinked to original sources

arXiv2026

Lightweight Generative Image Semantic Communication over Packet Erasure Channels

This paper addresses packet loss in semantic communication caused by network congestion or channel fluctuations. We propose LGSemCom, a lightweight generative packet-level joint source-channel coding (JSCC) framework for efficient and robust image transmission over packet erasure channels. Unlike conventional distortion-oriented recovery methods that yield blurry averages over erased regions, LGSemCom reformulates image recovery under packet erasures as a conditional generative task. By leveraging adversarial learning, the proposed decoder synthesizes plausible details without increasing complexity during inference. A key innovation of our framework is an erasure-aware weighting (EAW) strategy, which prioritizes generation in erased regions while preserving pixel fidelity in correctly received areas. To ensure computational efficiency, LGSemCom employs a fully convolutional codec based on efficient long-range attention blocks (ELABs) that capture global semantic dependencies with low complexity. Extensive experiments show that LGSemCom achieves superior perceptual reconstruction quality compared with existing benchmarks under severe packet loss, while achieving an order of magnitude faster inference. These attributes make LGSemCom highly suitable for latency-sensitive applications on resource-constrained edge devices.

eess.SP↗

arXiv2026

Learning-Driven Channel Representation for Wireless Localization: From Channel Observations to Location Inference

Wireless observations capture radio signal responses formed through interactions with propagation environments and spatial geometry. In integrated sensing and communication, such observations have become an important basis for high-accuracy localization beyond conventional channel estimation. Learning-driven methods learn implicit relations between channel propagation and spatial position, enabling location inference under complex channel conditions. However, the useful information is tightly coupled with environmental layout, temporal dynamics, hardware differences, and system configurations. This coupling obscures the inference process and weakens performance consistency across scenarios. In this paper, we model the localization process as a unified ``wireless observation--channel representation--location inference'' framework, and review learning-driven high-accuracy localization techniques with channel representations as the organizing view. The survey covers typical channel observation forms and analyzes their physical meanings. We also review channel feature extraction and representation learning methods, and summarize methods according to the acquisition, organization, adaptation, and reuse of channel representations. Typical methods are compared in terms of accuracy, applicable conditions, data requirements, and generalization. We highlight that the quality and usability of channel representations are critical to exploiting propagation information, and thus play a decisive role in localization performance. Finally, we summarize the key challenges in moving from experimental studies to real deployment and present our perspectives on these issues.

eess.SP↗

arXiv2026

A Transportable Threshold-Based Framework for Interpretable Classification of Medical Data

Black-box models limit the adoption of artificial intelligence in medicine due to their lack of interpretability and reproducibility. We introduce a statistically grounded framework that provides fully interpretable, rule-based clinical classification using the Bernoulli Na\"ive Bayes (BNB) model. The method applies supervised $\chi^2$-guided statistical binarization to continuous variables, identifying thresholds that maximize association with clinical outcomes within the training data. This transformation allows BNB to operate effectively on continuous medical data without sacrificing its inherent transparency. The approach was evaluated on three benchmark datasets, Pima Indians Diabetes, Wisconsin Breast Cancer, and Heart Failure Prediction, achieving area-under-the-curve (AUC) scores of 0.800 for the Pima analysis, 0.984 for Wisconsin Breast Cancer, and 0.919 for Heart Failure Prediction. In addition to discrimination, probabilistic reliability was assessed using leakage-safe cross-validated calibration analysis including Brier score, calibration intercept/slope, and post-hoc beta calibration, which improved probability calibration across datasets. These results suggest that a statistically interpretable framework can achieve performance comparable to more complex models while providing explicit, clinically meaningful decision rules and calibrated risk estimates. To illustrate this transparency concretely, a complete worked example demonstrates that model inference can be reproduced using only a reference table and basic arithmetic, without access to software or proprietary tools. This work offers a practical approach to supporting trustworthy and generalizable AI in real-world healthcare settings.

cs.LG↗

arXiv2025

Maximum a Posteriori Probability (MAP) Joint Carrier Frequency Offset (CFO) and Channel Estimation for MIMO Channels with Spatial and Temporal Correlations

We consider time varying MIMO fading channels with known spatial and temporal correlation and solve the problem of joint carrier frequency offset (CFO) and channel estimation with prior distributions. The maximum a posteriori probability (MAP) joint estimation is proved to be equivalent to a separate MAP estimation of the CFO followed by minimum mean square error (MMSE) estimation of the channel while treating the estimated CFO as true. The MAP solution is useful to take advantage of the estimates from the previous data packet. A low complexity universal CFO estimation algorithm is extended from the time invariant case to the time varying case. Unlike past algorithms, the universal algorithm does not need phase unwrapping to take advantage of the full range of symbol correlation and achieves the derived Bayesian Cram\'er-Rao lower bound (BCRLB) in almost all SNR range. We provide insight on the the relation among the temporal correlation coefficient of the fading, the CFO estimation performance, and the pilot signal structure. An unexpected observation is that the BCRLB is not a monotone function of the temporal correlation and is strongly influenced by the pilot signal structures. A simple rearrangement of the 0's and 1's in the pilot signal matrix will render the BCRLB from being non-monotone to being monotone in certain temporal correlation ranges. Since the BCRLB is shown to be achieved by the proposed algorithm, it provides a guideline for pilot signal design.

cs.IT↗

arXiv2024

Accelerating Point Cloud Ground Segmentation: From Mechanical to Solid-State Lidars

In this study, we propose a novel parallel processing method for point cloud ground segmentation, aimed at the technology evolution from mechanical to solid-state Lidar (SSL). We first benchmark point-based, grid-based, and range image-based ground segmentation algorithms using the SemanticKITTI dataset. Our results indicate that the range image-based method offers superior performance and robustness, particularly in resilience to frame slicing. Implementing the proposed algorithm on an FPGA demonstrates significant improvements in processing speed and scalability of resource usage. Additionally, we develop a custom dataset using camera-SSL equipment on our test vehicle to validate the effectiveness of the parallel processing approach for SSL frames in real world, achieving processing rates up to 30.9 times faster than CPU implementations. These findings underscore the potential of parallel processing strategies to enhance Lidar technologies for advanced perception tasks in autonomous vehicles and robotics. The data and code will be available post-publication on our GitHub repository: \url{https://github.com/WPI-APA-Lab/GroundSeg-Solid-State-Lidar-Parallel-Processing}

cs.CV↗

arXiv2024

SCEP: a Cosmic Magnetic Monopole Search Experiment

Magnetic monopole is a well-motivated class of beyond-Standard-Model particles that could provide insights into the long-standing puzzle of the quantization of electric charge. These hypothetical particles are likely to be super heavy ($\sim$10$^{15}$ GeV) and be produced in the very early stages of the Universe's evolution. We propose a novel detection scenario for the search of such cosmic magnetic monopoles, utilizing a hybrid approach that combines radio-frequency atomic magnetometers and plastic scintillators. Such setup allows for the collection of both the induction and scintillation signals generated by the passage of a magnetic monopole, which provides acceptance to the magnetic monopoles with their velocities larger than about 10$^{-6}$ light speed (assuming a signal-to-noise ratio of $\sim$4) and their masses larger than approximately 10$^7$ GeV (at $β\sim10^{-3}$). The proposed detector design has the potential to scale up to large area, enabling the exploration of the parameter space of the cosmic magnetic monopole beyond the current experimental and astrophysical constraints. It is estimated that such detector can reach current most stringent limits of the flux set by previous searches, with a signal-to-noise ratio of the induction signal larger than about 4.5, assuming an effective exposure being 20000 year$\cdot$m$^2$ and coil layer of 3.

hep-ex↗

arXiv2024

Stream-Based Ground Segmentation for Real-Time LiDAR Point Cloud Processing on FPGA

This paper presents a novel and fast approach for ground plane segmentation in a LiDAR point cloud, specifically optimized for processing speed and hardware efficiency on FPGA hardware platforms. Our approach leverages a channel-based segmentation method with an advanced angular data repair technique and a cross-eight-way flood-fill algorithm. This innovative approach significantly reduces the number of iterations while ensuring the high accuracy of the segmented ground plane, which makes the stream-based hardware implementation possible. To validate the proposed approach, we conducted extensive experiments on the SemanticKITTI dataset. We introduced a bird's-eye view (BEV) evaluation metric tailored for the area representation of LiDAR segmentation tasks. Our method demonstrated superior performance in terms of BEV areas when compared to the existing approaches. Moreover, we presented an optimized hardware architecture targeted on a Zynq-7000 FPGA, compatible with LiDARs of various channel densities, i.e., 32, 64, and 128 channels. Our FPGA implementation operating at 160 MHz significantly outperforms the traditional computing platforms, which is 12 to 25 times faster than the CPU-based solutions and up to 6 times faster than the GPU-based solution, in addition to the benefit of low power consumption.

eess.SP↗

arXiv2024

CoFi: Coarse-to-Fine ICP for LiDAR Localization in an Efficient Long-lasting Point Cloud Map

LiDAR odometry and localization has attracted increasing research interest in recent years. In the existing works, iterative closest point (ICP) is widely used since it is precise and efficient. Due to its non-convexity and its local iterative strategy, however, ICP-based method easily falls into local optima, which in turn calls for a precise initialization. In this paper, we propose CoFi, a Coarse-to-Fine ICP algorithm for LiDAR localization. Specifically, the proposed algorithm down-samples the input point sets under multiple voxel resolution, and gradually refines the transformation from the coarse point sets to the fine-grained point sets. In addition, we propose a map based LiDAR localization algorithm that extracts semantic feature points from the LiDAR frames and apply CoFi to estimate the pose on an efficient point cloud map. With the help of the Cylinder3D algorithm for LiDAR scan semantic segmentation, the proposed CoFi localization algorithm demonstrates the state-of-the-art performance on the KITTI odometry benchmark, with significant improvement over the literature.

cs.CV↗

arXiv2023

Real-Time Joint Simulation of LiDAR Perception and Motion Planning for Automated Driving

Real-time perception and motion planning are two crucial tasks for autonomous driving. While there are many research works focused on improving the performance of perception and motion planning individually, it is still not clear how a perception error may adversely impact the motion planning results. In this work, we propose a joint simulation framework with LiDAR-based perception and motion planning for real-time automated driving. Taking the sensor input from the CARLA simulator with additive noise, a LiDAR perception system is designed to detect and track all surrounding vehicles and to provide precise orientation and velocity information. Next, we introduce a new collision bound representation that relaxes the communication cost between the perception module and the motion planner. A novel collision checking algorithm is implemented using line intersection checking that is more efficient for long distance range in comparing to the traditional method of occupancy grid. We evaluate the joint simulation framework in CARLA for urban driving scenarios. Experiments show that our proposed automated driving system can execute at 25 Hz, which meets the real-time requirement. The LiDAR perception system has high accuracy within 20 meters when evaluated with the ground truth. The motion planning results in consistent safe distance keeping when tested in CARLA urban driving scenarios.

cs.RO↗

arXiv2023

PRISE: Demystifying Deep Lucas-Kanade with Strongly Star-Convex Constraints for Multimodel Image Alignment

The Lucas-Kanade (LK) method is a classic iterative homography estimation algorithm for image alignment, but often suffers from poor local optimality especially when image pairs have large distortions. To address this challenge, in this paper we propose a novel Deep Star-Convexified Lucas-Kanade (PRISE) method for multimodel image alignment by introducing strongly star-convex constraints into the optimization problem. Our basic idea is to enforce the neural network to approximately learn a star-convex loss landscape around the ground truth give any data to facilitate the convergence of the LK method to the ground truth through the high dimensional space defined by the network. This leads to a minimax learning problem, with contrastive (hinge) losses due to the definition of strong star-convexity that are appended to the original loss for training. We also provide an efficient sampling based algorithm to leverage the training cost, as well as some analysis on the quality of the solutions from PRISE. We further evaluate our approach on benchmark datasets such as MSCOCO, GoogleEarth, and GoogleMap, and demonstrate state-of-the-art results, especially for small pixel errors. Code can be downloaded from https://github.com/Zhang-VISLab.

cs.CV↗

arXiv2023

Self-supervised Geometric Features Discovery via Interpretable Attentio for Vehicle Re-Identification and Beyond (Complete Version)

To learn distinguishable patterns, most of recent works in vehicle re-identification (ReID) struggled to redevelop official benchmarks to provide various supervisions, which requires prohibitive human labors. In this paper, we seek to achieve the similar goal but do not involve more human efforts. To this end, we introduce a novel framework, which successfully encodes both geometric local features and global representations to distinguish vehicle instances, optimized only by the supervision from official ID labels. Specifically, given our insight that objects in ReID share similar geometric characteristics, we propose to borrow self-supervised representation learning to facilitate geometric features discovery. To condense these features, we introduce an interpretable attention module, with the core of local maxima aggregation instead of fully automatic learning, whose mechanism is completely understandable and whose response map is physically reasonable. To the best of our knowledge, we are the first that perform self-supervised learning to discover geometric features. We conduct comprehensive experiments on three most popular datasets for vehicle ReID, i.e., VeRi-776, CityFlow-ReID, and VehicleID. We report our state-of-the-art (SOTA) performances and promising visualization results. We also show the excellent scalability of our approach on other ReID related tasks, i.e., person ReID and multi-target multi-camera (MTMC) vehicle tracking. The code is available at https://github.com/ ming1993li/Self-supervised-Geometric.

cs.CV↗

arXiv2023

Self-supervised Geometric Features Discovery via Interpretable Attention for Vehicle Re-Identification and Beyond

cs.CV↗

arXiv2022

A Near Sensor Edge Computing System for Point Cloud Semantic Segmentation

Point cloud semantic segmentation has attracted attentions due to its robustness to light condition. This makes it an ideal semantic solution for autonomous driving. However, considering the large computation burden and bandwidth demanding of neural networks, putting all the computing into vehicle Electronic Control Unit (ECU) is not efficient or practical. In this paper, we proposed a light weighted point cloud semantic segmentation network based on range view. Due to its simple pre-processing and standard convolution, it is efficient when running on deep learning accelerator like DPU. Furthermore, a near sensor computing system is built for autonomous vehicles. In this system, a FPGA-based deep learning accelerator core (DPU) is placed next to the LiDAR sensor, to perform point cloud pre-processing and segmentation neural network. By leaving only the post-processing step to ECU, this solution heavily alleviate the computation burden of ECU and consequently shortens the decision making and vehicles reaction latency. Our semantic segmentation network achieved 10 frame per second (fps) on Xilinx DPU with computation efficiency 42.5 GOP/W.

cs.CV↗

arXiv2022

Enabling 3D Object Detection with a Low-Resolution LiDAR

Light Detection And Ranging (LiDAR) has been widely used in autonomous vehicles for perception and localization. However, the cost of a high-resolution LiDAR is still prohibitively expensive, while its low-resolution counterpart is much more affordable. Therefore, using low-resolution LiDAR for autonomous driving is an economically viable solution, but the point cloud sparsity makes it extremely challenging. In this paper, we propose a two-stage neural network framework that enables 3D object detection using a low-resolution LiDAR. Taking input from a low-resolution LiDAR point cloud and a monocular camera image, a depth completion network is employed to produce dense point cloud that is subsequently processed by a voxel-based network for 3D object detection. Evaluated with KITTI dataset for 3D object detection in Bird-Eye View (BEV), the experimental result shows that the proposed approach performs significantly better than directly applying the 16-line LiDAR point cloud for object detection. For both easy and moderate cases, our 3D vehicle detection results are close to those using 64-line high-resolution LiDARs.

cs.CV↗

arXiv2021

EllipsoidNet: Ellipsoid Representation for Point Cloud Classification and Segmentation

Point cloud patterns are hard to learn because of the implicit local geometry features among the orderless points. In recent years, point cloud representation in 2D space has attracted increasing research interest since it exposes the local geometry features in a 2D space. By projecting those points to a 2D feature map, the relationship between points is inherited in the context between pixels, which are further extracted by a 2D convolutional neural network. However, existing 2D representing methods are either accuracy limited or time-consuming. In this paper, we propose a novel 2D representation method that projects a point cloud onto an ellipsoid surface space, where local patterns are well exposed in ellipsoid-level and point-level. Additionally, a novel convolutional neural network named EllipsoidNet is proposed to utilize those features for point cloud classification and segmentation applications. The proposed methods are evaluated in ModelNet40 and ShapeNet benchmarks, where the advantages are clearly shown over existing 2D representation methods.

cs.CV↗

arXiv2021

A Divide-and-Merge Point Cloud Clustering Algorithm for LiDAR Panoptic Segmentation

Clustering objects from the LiDAR point cloud is an important research problem with many applications such as autonomous driving. To meet the real-time requirement, existing research proposed to apply the connected-component-labeling (CCL) technique on LiDAR spherical range image with a heuristic condition to check if two neighbor points are connected. However, LiDAR range image is different from a binary image which has a deterministic condition to tell if two pixels belong to the same component. The heuristic condition used on the LiDAR range image only works empirically, which suggests the LiDAR clustering algorithm should be robust to potential failures of the empirical heuristic condition. To overcome this challenge, this paper proposes a divide-and-merge LiDAR clustering algorithm. This algorithm firstly conducts clustering in each evenly divided local region, then merges the local clustered small components by voting on edge point pairs. Assuming there are $N$ LiDAR points of objects in total with $m$ divided local regions, the time complexity of the proposed algorithm is $O(N)+O(m^2)$. A smaller $m$ means the voting will involve more neighbor points, but the time complexity will become larger. So the $m$ controls the trade-off between the time complexity and the clustering accuracy. A proper $m$ helps the proposed algorithm work in real-time as well as maintain good performance. We evaluate the divide-and-merge clustering algorithm on the SemanticKITTI panoptic segmentation benchmark by cascading it with a state-of-the-art semantic segmentation model. The final performance evaluated through the leaderboard achieves the best among all published methods. The proposed algorithm is implemented with C++ and wrapped as a python function. It can be easily used with the modern deep learning framework in python.

cs.CV↗

arXiv2021

FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decoding

Projecting the point cloud on the 2D spherical range image transforms the LiDAR semantic segmentation to a 2D segmentation task on the range image. However, the LiDAR range image is still naturally different from the regular 2D RGB image; for example, each position on the range image encodes the unique geometry information. In this paper, we propose a new projection-based LiDAR semantic segmentation pipeline that consists of a novel network structure and an efficient post-processing step. In our network structure, we design a FID (fully interpolation decoding) module that directly upsamples the multi-resolution feature maps using bilinear interpolation. Inspired by the 3D distance interpolation used in PointNet++, we argue this FID module is a 2D version distance interpolation on $(θ, ϕ)$ space. As a parameter-free decoding module, the FID largely reduces the model complexity by maintaining good performance. Besides the network structure, we empirically find that our model predictions have clear boundaries between different semantic classes. This makes us rethink whether the widely used K-nearest-neighbor post-processing is still necessary for our pipeline. Then, we realize the many-to-one mapping causes the blurring effect that some points are mapped into the same pixel and share the same label. Therefore, we propose to process those occluded points by assigning the nearest predicted label to them. This NLA (nearest label assignment) post-processing step shows a better performance than KNN with faster inference speed in the ablation study. On the SemanticKITTI dataset, our pipeline achieves the best performance among all projection-based methods with $64 \times 2048$ resolution and all point-wise solutions. With a ResNet-34 as the backbone, both the training and testing of our model can be finished on a single RTX 2080 Ti with 11G memory. The code is released.

cs.CV↗

arXiv2021

A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation

LiDAR panoptic segmentation is a newly proposed technical task for autonomous driving. In contrast to popular end-to-end deep learning solutions, we propose a hybrid method with an existing semantic segmentation network to extract semantic information and a traditional LiDAR point cloud cluster algorithm to split each instance object. We argue geometry-based traditional clustering algorithms are worth being considered by showing a state-of-the-art performance among all published end-to-end deep learning solutions on the panoptic segmentation leaderboard of the SemanticKITTI dataset. To our best knowledge, we are the first to attempt the point cloud panoptic segmentation with clustering algorithms. Therefore, instead of working on new models, we give a comprehensive technical survey in this paper by implementing four typical cluster methods and report their performances on the benchmark. Those four cluster methods are the most representative ones with real-time running speed. They are implemented with C++ in this paper and then wrapped as a python function for seamless integration with the existing deep learning frameworks. We release our code for peer researchers who might be interested in this problem.

cs.CV↗