Spiking Neural Networks (SNNs) often rely on rate coding, where high-precision inference depends on long time-steps, leading to significant latency and energy cost—especially for ANN-to-SNN conversions. To address this, we propose Adaptive Fission, a post-training encoding technique that selectively splits high-sensitivity neurons into groups with varying scales and weights. This enables neuron-specific, on-demand precision and threshold allocation while introducing minimal spatial overhead. As a generalized form of population coding, it seamlessly applies to a wide range of pretrained SNN architectures without requiring additional training or fine-tuning. Experiments on neuromorphic hardware demonstrate up to 80\% reductions in latency and power consumption without degrading accuracy.

Copy number variation (CNV) is a crucial biomarker for many complex traits and diseases. Although numerous CNV detection tools are available, no single method consistently achieves optimal performance across diverse sequencing samples, as each tool has distinct advantages and limitations. Therefore, integrating the strengths of these tools to improve CNV detection accuracy is both a promising strategy and a significant challenge. To address this, we propose EMcnv, a novel deep ensemble framework based on meta-learning. EMcnv combines multiple CNV detection strategies through a three-step approach: (i) leveraging meta-learning and meta-path heterogeneous graphs, employing Relational Graph Convolutional Networks as a specific model within the Heterogeneous Graph Neural Networks framework to develop a probabilistic weight meta-model that ensembles various CNV detection strategies; (ii) assigning probabilistic weights to calls from different CNV detection tools and aggregating them into weighted CNV regions (CNVRs); (iii) refining Copy number variations based on weighted CNVRs. We conducted comprehensive experiments on both simulated and real sequencing data using benchmark datasets. The results demonstrate that EMcnv significantly outperforms popular existing methods, underscoring its superiority and importance in CNV detection. To support further research, the source code is available for academic use at https://github.com/Sherwin-xjtu/EMcnv.

With the increasing number of indications for immune checkpoint inhibitors in early and advanced cancers, the prospect of a tumor-agnostic biomarker to prioritize patients is compelling. Tumor mutation burden (TMB) is a widely endorsed biomarker that quantifies nonsynonymous mutations within tumor DNA, essential for neoantigen production, which, in turn, correlates with the immune response and guides decision-making. However, the general clinical application of TMB-relying on simple mutational counts targeted at a single endpoint-does not adequately capture the complex clonal structure of tumors nor the multifaceted nature of prognostic indicators. This recognition has spurred the exploration of sophisticated high-dimensional regression techniques. Unfortunately, the limited cohort sizes in immunotherapy trials have hindered the full potential of these advanced methods. Our approach considers patient subgroups as related yet distinct entities, enabling precise tailoring and refinement to address subgroup-specific dynamics. Given the deficiencies and the constraints, we introduce a TMB heterogeneity-optimized regression (THOR). This innovative model enhances the predictive capabilities of TMB by integrating tumor clonality and a diverse spectrum of clinical endpoints, further augmented by fusion techniques across subgroups to facilitate robust data sharing and interpretation. Our simulations validate THOR's superiority in parameter estimation for statistical inference. Clinically, we assess the utility of THOR in a structured cohort of 238 cancer patients undergoing immunotherapy, supplemented by 2212 patients across 19 subgroups from public datasets. The forecast of the responses and comparison of survival hazards demonstrate that THOR significantly enhances patient stratification and prognostic predictions by incorporating complex immunogenetic biology and subgroup-specific dynamics.

Detection of structural variations (SVs) through circulating tumor DNA (ctDNA) has become a key method for detecting minimal residual disease (MRD). However, the heterogeneity of ctDNA samples, characterized by variable limits of detection (LOD) and diverse structural variant types, significantly impacts detection stability and performance, posing persistent challenges for conventional SV detection tools such as Delly and Manta. These widely used methods require extensive manual parameter tuning, hindered by the combinatorial complexity of multiple parameters and heterogeneous sequencing data. To address this, we propose MRDadaptis, a novel SV detection tool that uniquely incorporates a self-adaptive parameter optimization mechanism. MRDadaptis distinguishes itself by integrating Bayesian optimization with meta-learning techniques to dynamically adjust detection parameters automatically, based on intrinsic features derived from the ctDNA sequencing data itself. This innovative approach not only reduces manual intervention but also effectively captures sample-specific characteristics, significantly improving detection stability, and detection performance. Extensive validation experiments using both simulated and real-world ctDNA datasets demonstrates it distinct advantages, including markedly improved average F1-scores and superior stability (reduced variance, lower RMSE, increased kurtosis). These results highlight the significant advantages of MRDadaptis in addressing sample heterogeneity, underscoring its potential to improve the accuracy and reliability of MRD detecting through ctDNA analysis. https://github.com/aAT0047/MRDadaptis.git.

Accurate tumor mutation burden (TMB) quantification is critical for immunotherapy stratification, yet remains challenging due to variability across sequencing platforms, tumor heterogeneity, and variant calling pipelines. Here, we introduce TMBquant, an explainable AI-powered caller designed to optimize TMB estimation through dynamic feature selection, ensemble learning, and automated strategy adaptation. Built upon the H2O AutoML framework, TMBquant integrates variant features, minimizes classification errors, and enhances both accuracy and stability across diverse datasets. We benchmarked TMBquant against nine widely used variant callers, including traditional tools (e.g. Mutect2, VarScan2, Strelka2) and recent AI-based methods (DeepSomatic, Octopus), using 706 whole-exome sequencing tumor-control pairs. To evaluate clinical relevance, we further assessed TMBquant through survival analyses across immunotherapy-treated cohorts of non-small cell lung cancer (NSCLC), nasopharyngeal carcinoma (NPC), and the two NSCLC subtypes: lung adenocarcinoma and lung squamous cell carcinoma. In each cohort, TMBquant consistently achieved the highest hazard ratios, demonstrating superior patient stratification compared to all other methods. Importantly, TMBquant maintained robust predictive performance across both high-TMB (NSCLC) and low-TMB (NPC) settings, highlighting its generalizability across cancer types with distinct biological characteristics. These findings establish TMBquant as a reliable, reproducible, and clinically actionable tool for precision oncology. The software is open source and freely available at https://github.com/SomaticCaller/SomaticCaller. To enhance reproducibility, we provide detailed usage instructions and representative code snippets for TMBquant in the Methods section (see Code Availability).

MOTIVATION: Minimal residual disease (MRD) as critical biomarker for cancer prognosis and management plays a crucial role in improving patient outcomes. However, detecting MRD via next-generation sequencing-based circulating tumor DNA variant calling remains unstable due to the extremely low variant allele frequency and significant inter- and intra-sample heterogeneity. Although parameter optimization can theoretically enhance the detection performance of variants, achieving stable MRD detection remains challenging due to three key factors: (i) the necessity for individualized parameter tuning across numerous heterogeneous genomic intervals within each sample, (ii) the tightly interdependent parameter requirements across different stages of variant detection workflows, and (iii) the limitations of current automated parameter optimization methods. RESULTS: In this study, we propose MRDagent, a novel variant detection tool designed specifically for MRD detection. MRDagent incorporates an iterative and self-adaptive optimization framework capable of handling unknown objectives, varying constraints, and highly coupled parameters across stages. A key innovation of MRDagent is the integration of a convolutional neural network-based meta-model, trained on historical data to enable rapid parameter prediction. This significantly enhances computational efficiency and generalization performance. Extensive evaluations on simulated and real-world datasets demonstrate MRDagent's superior and stable performance, providing an efficient, reliable solution for MRD detection in clinical and high-throughput research applications. AVAILABILITY AND IMPLEMENTATION: MRDagent is freely available at https://github.com/aAT0047/MRDagent.git. The corresponding dataset and software archive are available at Zenodo: https://doi.org/10.5281/zenodo.15458496.

Molecular residual disease (MRD) detection, initially developed for hematologic malignancies, has become a critical biomarker for monitoring solid tumors. MRD detection primarily relies on circulating tumor DNA (ctDNA) analysis using next-generation sequencing, offering high sensitivity and broad genomic coverage. However, challenges remain in designing cost-effective panels that maximize mutation detection while maintaining biological relevance. Fixed panels often lack sufficient patient-specific mutation coverage, while WES-based personalized MRD assays, despite their high sensitivity, are costly and less accessible. We developed a tumor comprehensive genomic profiling (CGP)-informed personalized MRD assay to detect tumor-derived mutations, which allowed us to design patient-specific personalized panels and meanwhile, provide a cost-effective alternative to whole exome sequencing (WES). To address these limitations, we developed MRDtarget, a heuristic multivariate Gaussian model-based targeted capture region selection method. By expanding beyond traditional hotspot regions, MRDtarget optimizes variant tracking for MRD detection, significantly improving sensitivity. Using a Bayesian inference-based heuristic approach, MRDtarget integrates multi-feature informativeness rates to identify optimal genomic regions for capture. Experimental results demonstrate that MRDtarget enables the detection of more variants per patient. This study underscores the importance of rational panel design to improve MRD sensitivity and provides a novel approach to enhance precision diagnostics and treatment for solid tumor patients.

In the face of emerging infectious diseases such as COVID-19, timely government intervention is crucial, as swift policy actions can effectively prevent greater losses. However, policymakers often need to balance multiple conflicting objectives. The lack of high-quality data and suitable analytical tools poses significant challenges for policy evaluation, especially in multi-objective decision-making, where accurately assessing the impact of interventions becomes even more difficult. To address this issue, this paper proposes a real-time data-driven policy monitoring method for dynamically tracking the effects of policy interventions. We introduce a new non-parametric one-sided test control chart, leveraging the interpretability and ease of implementation of control charts to monitor risk levels across various policies. Experimental results demonstrate the effectiveness of this method in policy monitoring.

In genome-wide association studies (GWAS) and candidate gene studies (CGS), appropriate multiple testing correction methods can effectively address linkage disequilibrium (LD) blocks and are crucial for controlling the family-wise error rate (FWER) while ensuring the reliability of results. In our recent research, we observed that when the number of independent effective Single Nucleotide Polymorphisms (SNPs) is relatively small, existing multiple testing correction methods struggle to effectively control the FWER at 0.05 and lack robustness. This study proposes an enhanced method that builds upon the Moskvina and Schmidt approach by incorporating additional SNP correlation information, allowing for precise control of the FWER at 0.05 with limited independent effective SNPs. We also leverage Monte Carlo integration on a GPU to accelerate the computation of significance thresholds. Our method was evaluated through both simulation studies and real genotype datasets, demonstrating superior performance and robustness compared to existing methods.

Variations in clinical and biological factors often lead to differing risks of dental implant failure among patients, even when undergoing similar procedures. Traditional predictive models often oversimplify outcomes into binary classifications, lacking the interpretability needed for accurate risk assessment and effective patient stratification. To address this challenge, this paper presents a novel multi-instance learning (MIL) framework that incorporates a Hosmer-Lemeshow-based loss function for implant failure risk assessment based on patient-specific clinical features. The framework also identifies the optimal threshold for key features, such as bone density, to enable robust patient stratification. The effectiveness label for each patient was constructed first and sampled patients into 600 and 1,000 groups. Results demonstrate that the proposed framework captures inter-patient variability with improved statistical calibration and enhanced risk stratification, offering practical insights to guide clinical decision-making. This study highlights the scalability and versatility of the proposed framework, bridging computational methodologies and practical applications in personalized implantology. Furthermore, the approach provides a transparent and effective tool for risk assessment, with potential applications in broader clinical stratification problems.

Monitoring rare health-related events using control charts is crucial for timely detecting potential changes in healthcare scenarios. For example, sequentially testing the level of changes in infectious disease patient numbers helps prepare before an epidemic. Unlike general health-related events, the observation of rare ones often involves an excess of zeros, making it more appropriate to use the zero-inflated Poisson (ZIP) distribution rather than the classical Poisson. Although residual-based charts have attracted significant attention in this field, few studies have explored how to appropriately select residuals with different advantages in the complex situations like healthcare scenarios. Therefore, in this paper, we propose LMR-EWMA, a least absolute shrinkage and selection operator (LASSO)-based multivariate residual exponentially weighted moving average (EWMA) control chart, to automatically select the optimal residuals for monitoring changes (i.e., shifts) in the number of rare health-related events. Additionally, we have innovatively designed a bi-directional moving mechanism to address the limitation of current research in distinguishing the practical significance of shifts. Experimental results on three simulation cases and two real datasets demonstrate that LMR-EWMA outperforms existing charts in monitoring performance.

Batch effects, caused by non-biological variations such as differences in laboratory conditions, reagent lots, or personnel, are a substantial source of noise in gene expression data. Accurately correcting these effects is crucial for valid biological inferences. However, the majority of existing batch effect correction algorithms are prone to overcorrection, where biologically meaningful signals are mistakenly identified as noise, especially in repeated measurement studies where time is confounded with batch. The failure to accurately distinguish between batch-related and biologically relevant variation leads to a loss of critical biological information. This paper presents RMComBat, an enhancement of the widely-used ComBat framework, which addresses this limitation by replacing the general linear model with a linear mixed-effects model. RMComBat incorporates subject-specific random intercepts to correct for sample correlation and enhance the preservation of biological signals. We tested RMComBat and several popular algorithms on simulated and real repeated measurement gene expression datasets, evaluating their performance through visual inspections and quantitative metrics. Results indicate that although most algorithms can reduce batch effects, they often do so at the cost of removing true biological signals. RMComBat demonstrates superior performance in preventing overcorrection, providing a more balanced and biologically informative correction in repeated measurement studies, so making it a valuable tool for improving the accuracy of gene expression analyses.

Accurate detection of copy number variations (CNVs) from sequencing data is crucial in many complex traits and diseases research. Although many CNV detection algorithms have been developed, challenges in precisely identifying CNVs persist. The core statistical model of these algorithms cannot self-adjust, which limits their adaptability to heterogeneous samples and reduces detection accuracy. address this challenge, we reframed the CNV detection problem as a quality control issue and incorporated adaptive mechanisms. We developed adapCNV, a novel adaptive CNV detection framework that integrates machine learning with optimization control. This framework enables dynamic adaptation of primary parameters based on sample features. We defined a quantifiable metric, RD fluctuation values, to assess signal characteristics when the algorithm accurately detects CNVs. We then employed machine learning techniques extract features from panel sequencing data, select initial parameter values for samples, and determine optimal RD fluctuation values. By adopting adaptive model predictive control (AMPC), adapCNV performs optimizations within rolling window. It dynamically adjusts the primary parameters based on error feedback from RD fluctuation values. This adaptive control strategy enables dynamic adjustment automatically match the characteristics of panel sequencing samples, significantly enhancing overall detection quality. The performance of this framework was validated with simulated data. Comparative analysis demonstrated that the proposed method outperforms the baseline approach, particularly in detecting small CNVs. The adapCNV framework is particularly suitable for panel sequencing, which may have broad applications in clinical practice. This novel approach from quality control perspective introduces a new paradigm for CNV detection.

Copy number variations are crucial in cancer research, but their detection through next-generation sequencing is often hindered by read biases, particularly in complex genomic regions. Existing bias-correction methods address common issues like GC content but often fail in regions with repetitive sequences or segmental duplications, leading to false-positive CNVs. We propose refMask, a hybrid Gaussian model-based method that dynamically identifies low-confidence regions in the reference genome, correcting read biases and improving CNV detection accuracy. By integrating features from hg38 and T2T genomes, refMask tailors a custom blacklist for each sequencing sample, enhancing the reliability of CNV detection across diverse conditions. Our method provides a more accurate and flexible solution compared to current fixed blacklists, offering improved performance in challenging genomic regions.

Spiking neural networks (SNNs) are energy-efficient and hold great potential for large-scale inference. Since training SNNs from scratch is costly and has limited performance, converting pretrained artificial neural networks (ANNs) to SNNs is an attractive approach that retains robust performance without additional training data and resources. However, while existing conversion methods work well on convolution networks, emerging Transformer models introduce unique mechanisms like self-attention and test-time normalization, leading to non-causal non-linear interactions unachievable by current SNNs. To address this, we approximate these operations in both temporal and spatial dimensions, thereby providing the first SNN conversion pipeline for Transformers. We propose \textit{Universal Group Operators} to approximate non-linear operations spatially and a \textit{Temporal-Corrective Self-Attention Layer} that approximates spike multiplications at inference through an estimation-correction approach. Our algorithm is implemented on a pretrained ViT-B/32 from CLIP, inheriting its zero-shot classification capabilities, while improving control over conversion losses. To our knowledge, this is the first direct training-free conversion of a pretrained Transformer to a purely event-driven SNN, promising for neuromorphic hardware deployment.

In cancer genomics, variant calling has advanced, but traditional mean accuracy evaluations are inadequate for biomarkers like tumor mutation burden, which vary significantly across samples, affecting immunotherapy patient selection and threshold settings. In this study, we introduce TMBstable, an innovative method that dynamically selects optimal variant calling strategies for specific genomic regions using a meta-learning framework, distinguishing it from traditional callers with uniform sample-wide strategies. The process begins with segmenting the sample into windows and extracting meta-features for clustering, followed by using a pre-trained meta-model to select suitable algorithms for each cluster, thereby addressing strategy-sample mismatches, reducing performance fluctuations and ensuring consistent performance across various samples. We evaluated TMBstable using both simulated and real non-small cell lung cancer and nasopharyngeal carcinoma samples, comparing it with advanced callers. The assessment, focusing on stability measures, such as the variance and coefficient of variation in false positive rate, false negative rate, precision and recall, involved 300 simulated and 106 real tumor samples. Benchmark results showed TMBstable's superior stability with the lowest variance and coefficient of variation across performance metrics, highlighting its effectiveness in analyzing the counting-based biomarker. The TMBstable algorithm can be accessed at https://github.com/hello-json/TMBstable for academic usage only.

Microsatellite instability (MSI) is an important genomic biomarker for cancer diagnosis and treatment, and sequencing-based approaches are often applied to identify MSI because of its fastness and efficiency. These approaches, however, may fail to identify MSI on one or more sub-clones for certain cancers with a high degree of heterogeneity, leading to erroneous diagnoses and unsuitable treatments. Besides, the computational cost of identifying sub-clonal MSI can be exponentially increased when multiple sub-clones with different length distributions share MSI status. Herein, this paper proposes "scMSI", an accurate and efficient estimation of sub-clonal MSI to identify the microsatellite status. scMSI is an integrative Bayesian method to deconvolute the mixed-length distribution of sub-clones by a novel alternating iterative optimization procedure based on a subtle generative model. During the process of deconvolution, the optimized division of each sub-clone is attained by a heuristic algorithm, aligning with clone proportions that adhere optimally to the sample's clonal structure. To evaluate the performance, 16 patients diagnosed with endometrial cancer, exhibiting positive responses to the treatment despite having negative MSI status based on sequencing-based approaches, were considered. Excitingly, scMSI reported MSI on sub-clones successfully, and the findings matched the conclusions on immunohistochemistry. In addition, testing results on a series of experiments with simulation datasets concerning a variety of impact factors demonstrated the effectiveness and superiority of scMSI in detecting MSI on sub-clones over existing approaches. scMSI provides a new way of detecting MSI for cancers with a high degree of heterogeneity.

Virtual content creation and interaction play an important role in modern 3D applications. Recovering detailed 3D models from real scenes can significantly expand the scope of its applications and has been studied for decades in the computer vision and computer graphics community. In this work, we propose Vox-Surf, a voxel-based implicit surface representation. Our Vox-Surf divides the space into finite sparse voxels, where each voxel is a basic geometry unit that stores geometry and appearance information on its corner vertices. Due to the sparsity inherited from the voxel representation, Vox-Surf is suitable for almost any scene and can be easily trained end-to-end from multiple view images. We utilize a progressive training process to gradually cull out empty voxels and keep only valid voxels for further optimization, which greatly reduces the number of sample points and improves inference speed. Experiments show that our Vox-Surf representation can learn fine surface details and accurate colors with less memory and faster rendering than previous methods. The resulting fine voxels can also be considered as the bounding volumes for collision detection, which is useful in 3D interactions. We also show the potential application of Vox-Surf in scene editing and augmented reality. The source code is publicly available at https://github.com/zju3dv/Vox-Surf.

Large-scale urban scene 3D mapping has urgent demands and wide applications in many areas, where sensor pose globalization remains its fundamental problem and critical step. As the street-view images and vehicle-borne Light Detection And Ranging (LiDAR) points contain complementary advantages in urban scene 3D mapping, it is desirable to make the most of both to facilitate this task. Most existing methods make strong assumptions of strict synchronization, and even further, exact calibration between the vehicle-borne cameras and LiDARs, which are hard to guarantee in practice. To deal with this, we propose a novel pipeline for vehicle-borne camera and LiDAR temporal and spatial pose globalization with the guidance of Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU), where both of the assumptions on strict synchronization and exact calibration are loosened. Specifically, the global poses of both cameras and LiDARs are first initialized by leveraging GNSS/IMU signals and multi-sensor pre-calibrations, and then refined by a global optimization scheme. To perform the global pose optimization, image-based, LiDAR-based, and cross-domain data association and constraint construction are conducted. Among them, the cross-domain ones, which are achieved by LiDAR point projection, image feature back-projection, and spatial point association, provide key clues for associating these two kinds of data with significant differences. Comprehensive experiments on both of a self-collected and the KITTI Odometry datasets demonstrate the effectiveness of our proposed method on multi-sensor pose globalization for large-scale urban scene 3D mapping.

Multiple sensors, especially cameras and LiDARs, are widely used in autonomous vehicles. In order to fuse data from different sensors accurately, precise calibrations are required, including camera intrinsic parameters, and relative poses between multiple cameras and LiDARs. However, most existing camera-LiDAR calibration methods need to place manually designed calibration objects in multiple locations and multiple times, which are time-consuming and labor-intensive, and are not suitable for frequent use. To address that, in this paper we proposed a novel calibration pipeline that can automatically calibrate multiple cameras and multiple LiDARs in a Structure-from-Motion (SfM) process. In our pipeline, we first perform a global SfM on all images with the help of rough LiDAR data to get the initial poses of all sensors. Then, feature points on lines and planes are extracted from both SfM point cloud and LiDARs. With these features, a global Bundle Adjustment is performed to minimize the point reprojection errors, point-to-line errors, and point-to-plane errors together. During this minimization process, camera intrinsic parameters, camera and LiDAR poses, and SfM point cloud are refined jointly. The proposed method uses the characteristics of natural scenes, does not require manually designed calibration objects, and incorporates all calibration parameters into a unified optimization framework. Experiments on autonomous vehicles with different sensor configurations demonstrate the effectiveness and robustness of the proposed method.

In this work, we present a dense tracking and mapping system named Vox-Fusion, which seamlessly fuses neural implicit representations with traditional volumetric fusion methods. Our approach is inspired by the recently developed implicit mapping and positioning system and further extends the idea so that it can be freely applied to practical scenarios. Specifically, we leverage a voxel-based neural implicit surface representation to encode and optimize the scene inside each voxel. Furthermore, we adopt an octree-based structure to divide the scene and support dynamic expansion, enabling our system to track and map arbitrary scenes without knowing the environment like in previous works. Moreover, we proposed a high-performance multi-process framework to speed up the method, thus supporting some applications that require real-time performance. The evaluation results show that our methods can achieve better accuracy and completeness than previous methods. We also show that our Vox-Fusion can be used in augmented reality and virtual reality applications. Our source code is publicly available at https://github.com/zju3dv/Vox-Fusion.

Copy number variation (CNV) is a class of key biomarkers in many complex traits and diseases. Detecting CNV from sequencing data is a substantial bioinformatics problem and a standard requirement in clinical practice. Although many proposed CNV detection approaches exist, the core statistical model at their foundation is weakened by two critical computational issues: (i) identifying the optimal setting on the sliding window and (ii) correcting for bias and noise. We designed a statistical process model to overcome these limitations by calculating regional read depths via an exponentially weighted moving average strategy. A one-run detection of CNVs of various lengths is then achieved by a dynamic sliding window, whose size is self-adopted according to the weighted averages. We also designed a novel bias/noise reduction model, accompanied by the moving average, which can handle complicated patterns and extend training data. This model, called PEcnv, accurately detects CNVs ranging from kb-scale to chromosome-arm level. The model performance was validated with simulation samples and real samples. Comparative analysis showed that PEcnv outperforms current popular approaches. Notably, PEcnv provided considerable advantages in detecting small CNVs (1 kb-1 Mb) in panel sequencing data. Thus, PEcnv fills the gap left by existing methods focusing on large CNVs. PEcnv may have broad applications in clinical testing where panel sequencing is the dominant strategy. Availability and implementation: Source code is freely available at https://github.com/Sherwin-xjtu/PEcnv.

Multiple instance learning (MIL) is semisupervised learning that predicts the label of a bag with a wide diversity of instances. It has many applications and thus attracts increasingly more attention. In this paper, we propose a new MIL algorithm using the structural information of a bag to predict its label. In the proposed method, a bag is transformed into a graph, and spectral clustering is employed to divide the graph into several subgraphs. Then, the graph Fourier transform is utilized to extract the features of the subgraphs. Finally, an end-to-end neural network is used to predict the label of a bag with the extracted features. An empirical study with 25 datasets was conducted to validate the effectiveness of the proposed method. The experimental results show that the proposed method performs better than the 6 baseline methods on most datasets.

Compared to widely used LiDAR-based mapping in autonomous driving field, image-based mapping method has the advantages of low cost, high resolution, and no need for complex calibration. However, the image-based 3D mapping depends heavily on the texture richness and always leaves holes and outliers in low-textured areas, such as the road surface. To this end, this paper proposed a novel semantically guided Multi-View Stereo method for dense 3D road mapping, which integrates semantic information into PatchMatch-based MVS pipeline and uses image semantic segmentation as soft constraints in neighbor views selection, depth-map initialization, depth propagation, and depth-map completion. Experimental results on public and our own datasets show that, with the help of semantics, the proposed method achieves superior completeness with comparable accuracy for 3D road mapping compared to state-of-the-art MVS methods.

LiDAR map matching(LMM) is a critical localization technique in autonomous driving while existing methods have problems in terms of both accuracy and robustness when driving in the scenes with poor structure information (e.g. highways). This paper put forward a multi-level intensity map based cascaded network for LiDAR map matching in autonomous driving. The network uses an effective multi-level intensity map representation to compactly encode the appearance and structure information of point clouds, which effectively reduce the position ambiguity in structure-less scenarios. Besides, this method leverages the multi-scale nature of deep neural networks and matches the online LiDAR observation with the offline map in a coarse-to-fine manner so as to balance the time-consuming and precision. Extensive experiments on diverse autonomous driving environments demonstrate the superiority of our proposed method over other existing state-of-the-art methods.

Estimating the 6-DoF camera pose of an image with respect to a 3D scene model, known as visual localization, is a fundamental problem in many computer vision and robotics tasks. Among various visual localization methods, the direct 2D-3D matching method has become the preferred method for many practical applications due to its computational efficiency. When using direct 2D-3D matching methods in large-scale scenes, a vocabulary tree can be used to accelerate the matching process, which will also induce the quantization artifacts leading to reduce the inlier ratio and decrease the localization accuracy. To this end, in this paper two simple and effective mechanisms, called visibility-based recalling and space-based recalling, are proposed to recover lost matches caused by the quantization artifacts, thus can largely improve the localization accuracy and success rate without increasing too much computational time. Experimental results on long-term visual localization benchmarks demonstrate the effectiveness of our method compared with state-of-the-arts.