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Siqi Shen

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18 papers

2 author rows

AAAI Conference 2026 Conference Paper

Physically-Based LiDAR Smoke Simulation for Robust 3D Object Detection

Shijun Zheng
Yu Guo
Weiquan Liu
Yu Zang
Siqi Shen
Ming Cheng
Cheng Wang

3D object detection in adverse weather is crucial for autonomous driving, especially in smoke where LiDAR data becomes sparse and noisy. Due to the lack of real smoke data, this paper introduces a physics-based simulation framework to generate realistic LiDAR point clouds of smoke and augment large-scale driving datasets. First, we present a 3D fluid dynamics-based smoke simulation framework in Unity, which models the realistic spatial diffusion and temporal evolution of smoke particles. Coupled with a physically accurate LiDAR perception module, our system captures complex light interactions—such as beam attenuation, scattering, and multi-path effects—to generate high-fidelity, physically consistent smoke point clouds. Second, we propose a range image-based data fusion strategy that seamlessly integrates the simulated smoke point clouds into large-scale real-world LiDAR datasets (e.g., Waymo). This approach accurately emulates LiDAR scanning characteristics and naturally incorporates occlusion effects, enabling realistic smoke integration without compromising spatial consistency. To validate our approach, we collect a real-world LiDAR smoke dataset (LiSmoke) and conduct extensive experiments using state-of-the-art 3D detectors. Results demonstrate that models trained with our augmented synthetic data achieve significant improvements in smoke-affected scenarios, while maintaining competitive performance in clear-weather conditions. Our work provides a cost-effective solution for enhancing perception robustness in safety-critical environments.

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AAAI Conference 2026 Conference Paper

Walking Further: Semantic-Aware Multimodal Gait Recognition Under Long-Range Conditions

Zhiyang Lu
Wen Jiang
Tianren Wu
Zhichao Wang
Changwang Zhang
Siqi Shen
Ming Cheng

Gait recognition is an emerging biometric technology that enables non-intrusive and hard-to-spoof human identification. However, most existing methods are confined to short-range, unimodal settings and fail to generalize to long-range and cross-distance scenarios under real-world conditions. To address this gap, we present LRGait, the first LiDAR-Camera multimodal benchmark designed for robust long-range gait recognition across diverse outdoor distances and environments. We further propose EMGaitNet, an end-to-end framework tailored for long-range multimodal gait recognition. To bridge the modality gap between RGB images and point clouds, we introduce a semantic-guided fusion pipeline. A CLIP-based Semantic Mining (SeMi) module first extracts human body-part-aware semantic cues, which are then employed to align 2D and 3D features via a Semantic-Guided Alignment (SGA) module within a unified embedding space. A Symmetric Cross-Attention Fusion (SCAF) module hierarchically integrates visual contours and 3D geometric features, and a Spatio-Temporal (ST) module captures global gait dynamics. Extensive experiments on various gait datasets validate the effectiveness of our method.

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AAAI Conference 2025 Conference Paper

A New Adversarial Perspective for LiDAR-based 3D Object Detection

Shijun Zheng
Weiquan Liu
Yu Guo
Yu Zang
Siqi Shen
Cheng Wang

Autonomous vehicles (AVs) rely on LiDAR sensors for environmental perception and decision-making in driving scenarios. However, ensuring the safety and reliability of AVs in complex environments remains a pressing challenge. To address this issue, we introduce a real-world dataset (ROLiD) comprising LiDAR-scanned point clouds of two random objects: water mist and smoke. In this paper, we introduce a novel adversarial perspective by proposing an attack framework that utilizes water mist and smoke to simulate environmental interference. Specifically, we propose a point cloud sequence generation method using a motion and content decomposition generative adversarial network named PCS-GAN to simulate the distribution of random objects. Furthermore, leveraging the simulated LiDAR scanning characteristics implemented with Range Image, we examine the effects of introducing random object perturbations at various positions on the target vehicle. Extensive experiments demonstrate that adversarial perturbations based on random objects effectively deceive vehicle detection and reduce the recognition rate of 3D object detection models.

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AAAI Conference 2025 Conference Paper

ConDo: Continual Domain Expansion for Absolute Pose Regression

Zijun Li
Zhipeng Cai
Bochun Yang
Xuelun Shen
Siqi Shen
Xiaoliang Fan
Michael Paulitsch
Cheng Wang

Visual localization is a fundamental machine learning problem. Absolute Pose Regression (APR) trains a scene-dependent model to efficiently map an input image to the camera pose in a pre-defined scene. However, many applications have continually changing environments, where inference data at novel poses or scene conditions (weather, geometry) appear after deployment. Training APR on a fixed dataset leads to overfitting, making it fail catastrophically on challenging novel data. This work proposes Continual Domain Expansion (ConDo), which continually collects unlabeled inference data to update the deployed APR. Instead of applying standard unsupervised domain adaptation methods which are ineffective for APR, ConDo effectively learns from unlabeled data by distilling knowledge from scene-agnostic localization methods. By sampling data uniformly from historical and newly collected data, ConDo can effectively expand the generalization domain of APR. Large-scale benchmarks with various scene types are constructed to evaluate models under practical (long-term) data changes. ConDo consistently and significantly outperforms baselines across architectures, scene types, and data changes. On challenging scenes (Fig.1), it reduces the localization error by >7x (14.8m vs 1.7m). Analysis shows the robustness of ConDo against compute budgets, replay buffer sizes and teacher prediction noise. Comparing to model re-training, ConDo achieves similar performance up to 25x faster.

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ICLR Conference 2025 Conference Paper

DoF: A Diffusion Factorization Framework for Offline Multi-Agent Reinforcement Learning

Chao Li
Ziwei Deng
Chenxing Lin
Wenqi Chen
Yongquan Fu
Weiquan Liu
Chenglu Wen
Cheng Wang 0003

Diffusion models have been widely adopted in image and language generation and are now being applied to reinforcement learning. However, the application of diffusion models in offline cooperative Multi-Agent Reinforcement Learning (MARL) remains limited. Although existing studies explore this direction, they suffer from scalability or poor cooperation issues due to the lack of design principles for diffusion-based MARL. The Individual-Global-Max (IGM) principle is a popular design principle for cooperative MARL. By satisfying this principle, MARL algorithms achieve remarkable performance with good scalability. In this work, we extend the IGM principle to the Individual-Global-identically-Distributed (IGD) principle. This principle stipulates that the generated outcome of a multi-agent diffusion model should be identically distributed as the collective outcomes from multiple individual-agent diffusion models. We propose DoF, a diffusion factorization framework for Offline MARL. It uses noise factorization function to factorize a centralized diffusion model into multiple diffusion models. We theoretically show that the noise factorization functions satisfy the IGD principle. Furthermore, DoF uses data factorization function to model the complex relationship among data generated by multiple diffusion models. Through extensive experiments, we demonstrate the effectiveness of DoF. The source code is available at [https://github.com/xmu-rl-3dv/DoF](https://github.com/xmu-rl-3dv/DoF).

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ICML Conference 2025 Conference Paper

GradPS: Resolving Futile Neurons in Parameter Sharing Network for Multi-Agent Reinforcement Learning

Haoyuan Qin
Zhengzhu Liu
Chenxing Lin
Chennan Ma
Songzhu Mei
Siqi Shen
Cheng Wang 0003

Parameter-sharing (PS) techniques have been widely adopted in cooperative Multi-Agent Reinforcement Learning (MARL). In PS, all the agents share a policy network with identical parameters, which enjoys good sample efficiency. However, PS could lead to homogeneous policies that limit MARL performance. We tackle this problem from the angle of gradient conflict among agents. We find that the existence of futile neurons whose update is canceled out by gradient conflicts among agents leads to poor learning efficiency and diversity. To address this deficiency, we propose GradPS, a gradient-based PS method. It dynamically creates multiple clones for each futile neuron. For each clone, a group of agents with low gradient-conflict shares the neuron’s parameters. Our method can enjoy good sample efficiency by sharing the gradients among agents of the same clone neuron. Moreover, it can encourage diverse behaviors through independently updating an exclusive clone neuron. Through extensive experiments, we show that GradPS can learn diverse policies with promising performance. The source code for GradPS is available in https: //github. com/xmu-rl-3dv/GradPS.

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NeurIPS Conference 2025 Conference Paper

L2RSI: Cross-view LiDAR-based Place Recognition for Large-scale Urban Scenes via Remote Sensing Imagery

Ziwei Shi
Xiaoran Zhang
Wenjing Xu
Yan Xia
Yu Zang
Siqi Shen
Cheng Wang

We tackle the challenge of LiDAR-based place recognition, which traditionally depends on costly and time-consuming prior 3D maps. To overcome this, we first construct LiRSI-XA dataset, which encompasses approximately $110, 000$ remote sensing submaps and $13, 000$ LiDAR point cloud submaps captured in urban scenes, and propose a novel method, L2RSI, for cross-view LiDAR place recognition using high-resolution Remote Sensing Imagery. This approach enables large-scale localization capabilities at a reduced cost by leveraging readily available overhead images as map proxies. L2RSI addresses the dual challenges of cross-view and cross-modal place recognition by learning feature alignment between point cloud submaps and remote sensing submaps in the semantic domain. Additionally, we introduce a novel probability propagation method based on particle estimation to refine position predictions, effectively leveraging temporal and spatial information. This approach enables large-scale retrieval and cross-scene generalization without fine-tuning. Extensive experiments on LiRSI-XA demonstrate that, within a $100km^2$ retrieval range, L2RSI accurately localizes $83. 27\%$ of point cloud submaps within a $30m$ radius for top-$1$ retrieved location. Our project page is publicly available at https: //shizw695. github. io/L2RSI/.

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NeurIPS Conference 2025 Conference Paper

PlanU: Large Language Model Reasoning through Planning under Uncertainty

Ziwei Deng
Mian Deng
Chenjing Liang
Zeming Gao
Chennan Ma
Chenxing Lin
Haipeng Zhang
Songzhu Mei

Large Language Models (LLMs) are increasingly being explored across a range of reasoning tasks. However, LLMs sometimes struggle with reasoning tasks under uncertainty that are relatively easy for humans, such as planning actions in stochastic environments. The adoption of LLMs for reasoning is impeded by uncertainty challenges, such as LLM uncertainty and environmental uncertainty. LLM uncertainty arises from the stochastic sampling process inherent to LLMs. Most LLM-based Decision-Making (LDM) approaches address LLM uncertainty through multiple reasoning chains or search trees. However, these approaches overlook environmental uncertainty, which leads to poor performance in environments with stochastic state transitions. Some recent LDM approaches deal with uncertainty by forecasting the probability of unknown variables. However, they are not designed for multi-step reasoning tasks that require interaction with the environment. To address uncertainty in LLM decision-making, we introduce PlanU, an LLM-based planning method that captures uncertainty within Monte Carlo Tree Search (MCTS). PlanU models the return of each node in the MCTS as a quantile distribution, which uses a set of quantiles to represent the return distribution. To balance exploration and exploitation during tree search, PlanU introduces an Upper Confidence Bounds with Curiosity (UCC) score which estimates the uncertainty of MCTS nodes. Through extensive experiments, we demonstrate the effectiveness of PlanU in LLM-based reasoning tasks under uncertainty.

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AAAI Conference 2025 Conference Paper

Text to Point Cloud Localization with Multi-Level Negative Contrastive Learning

Dunqiang Liu
Shujun Huang
Wen Li
Siqi Shen
Cheng Wang

Language-based localization is a crucial task in robotics and computer vision, enabling robots to understand spatial positions through language. Recent methods rely on contrastive learning to establish correspondences between global features of texts and point clouds. However, the inherent ambiguity of textual descriptions makes it difficult to convey geometric information accurately, forcing alignment of them in the feature space may compromise the expressiveness of the point clouds. Unlike previous methods, this paper proposes using language as a filter to distinguish dissimilar locations. To this end, we propose a robust framework of multi-level negative contrastive learning for language-based localization, fully leveraging the descriptive power of language for spatial localization. Our method learns multiple mismatched factors by minimizing the similarity of different locations at different levels, including global-level, instance-level and relationlevel, respectively. Extensive experiments conducted on the KITTI360Pose benchmark demonstrate that our method outperforms better that the state-of-the-art methods. Specifically, we achieve a 56.3% improvement in Top-1 retrieval recall and a 45.9% improvement in 5m localization recall.

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IJCAI Conference 2024 Conference Paper

Bridging LiDAR Gaps: A Multi-LiDARs Domain Adaptation Dataset for 3D Semantic Segmentation

Shaoyang Chen
Bochun Yang
Yan Xia
Ming Cheng
Siqi Shen
Cheng Wang

We focus on the domain adaptation problem for 3D semantic segmentation, addressing the challenge of data variability in point clouds collected by different LiDARs. Existing benchmarks often mix different types of datasets, which blurs and complicates segmentation evaluations. Here, we introduce a Multi-LiDARs Domain Adaptation Segmentation (MLDAS) dataset, which contains point-wise semantic annotated point clouds captured simultaneously by a 128-beam LiDAR, a 64-beam LiDAR, a 32-beam LiDAR. We select 31, 875 scans from 2 representative scenarios: campus and urban street. Furthermore, we evaluate the current 3D segmentation unsupervised domain adaptation methods on the proposed dataset and propose Hierarchical Segmentation Network with Spatial Consistency (HSSC) as a novel knowledge transfer method to mitigate the domain gap significantly using spatial-temporal consistency constraints. Extensive experiments show that HSSC greatly improves the state-of-the-art cross-domain semantic segmentation methods. Our project is available at https: //sychen320. github. io/projects/MLDAS.

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NeurIPS Conference 2024 Conference Paper

Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud Registration

Kezheng Xiong
Haoen Xiang
Qingshan Xu
Chenglu Wen
Siqi Shen
Jonathan Li
Cheng Wang

Point cloud registration, a fundamental task in 3D vision, has achieved remarkable success with learning-based methods in outdoor environments. Unsupervised outdoor point cloud registration methods have recently emerged to circumvent the need for costly pose annotations. However, they fail to establish reliable optimization objectives for unsupervised training, either relying on overly strong geometric assumptions, or suffering from poor-quality pseudo-labels due to inadequate integration of low-level geometric and high-level contextual information. We have observed that in the feature space, latent new inlier correspondences tend to clusteraround respective positive anchors that summarize features of existing inliers. Motivated by this observation, we propose a novel unsupervised registration method termed INTEGER to incorporate high-level contextual information for reliable pseudo-label mining. Specifically, we propose the Feature-Geometry Coherence Mining module to dynamically adapt the teacher for each mini-batch of data during training and discover reliable pseudo-labels by considering both high-level feature representations and low-level geometric cues. Furthermore, we propose Anchor-Based Contrastive Learning to facilitate contrastive learning with anchors for a robust feature space. Lastly, we introduce a Mixed-Density Student to learn density-invariant features, addressing challenges related to density variation and low overlap in the outdoor scenario. Extensive experiments on KITTI and nuScenes datasets demonstrate that our INTEGER achieves competitive performance in terms of accuracy and generalizability.

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AAAI Conference 2024 Conference Paper

Neighborhood-Enhanced 3D Human Pose Estimation with Monocular LiDAR in Long-Range Outdoor Scenes

Jingyi Zhang
Qihong Mao
Guosheng Hu
Siqi Shen
Cheng Wang

3D human pose estimation (3HPE) in large-scale outdoor scenes using commercial LiDAR has attracted significant attention due to its potential for real-life applications. However, existing LiDAR-based methods for 3HPE primarily rely on recovering 3D human poses from individual point clouds, and the coherence cues present in the neighborhood are not sufficiently harnessed. In this work, we explore spatial and contexture coherence cues contained in the neighborhood that lead to great performance improvements in 3HPE. Specifically, firstly, we deeply investigate the 3D neighbor in the background (3BN) which serves as a spatial coherence cue for inferring reliable motion since it provides physical laws to limit motion targets. Secondly, we introduce a novel 3D scanning neighbor (3SN) generated during the data collection and 3SN implies structural edge coherence cues. We use 3SN to overcome the degradation of performance and data quality caused by the sparsity-varying properties of LiDAR point clouds. In order to effectively model the complementation between these distinct cues and build consistent temporal relationships across human motions, we propose a new transformer-based module called the CoherenceFuse module. Extensive experiments were conducted on publicly available datasets, namely LidarHuman26M, CIMI4D, SLOPER4D and Waymo Open Dataset v2.0, showcase the superiority and effectiveness of our proposed method. In particular, when compared with LidarCap on the LidarHuman26M dataset, our method demonstrates a reduction of 7.08mm in the average MPJPE metric, along with a decrease of 16.55mm in the MPJPE metric for distances exceeding 25 meters. The code and models are available at https://github.com/jingyi-zhang/Neighborhood-enhanced-LidarCap.

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AAAI Conference 2024 Conference Paper

SPEAL: Skeletal Prior Embedded Attention Learning for Cross-Source Point Cloud Registration

Kezheng Xiong
Maoji Zheng
Qingshan Xu
Chenglu Wen
Siqi Shen
Cheng Wang

Point cloud registration, a fundamental task in 3D computer vision, has remained largely unexplored in cross-source point clouds and unstructured scenes. The primary challenges arise from noise, outliers, and variations in scale and density. However, neglected geometric natures of point clouds restricts the performance of current methods. In this paper, we propose a novel method termed SPEAL to leverage skeletal representations for effective learning of intrinsic topologies of point clouds, facilitating robust capture of geometric intricacy. Specifically, we design the Skeleton Extraction Module to extract skeleton points and skeletal features in an unsupervised manner, which is inherently robust to noise and density variances. Then, we propose the Skeleton-Aware GeoTransformer to encode high-level skeleton-aware features. It explicitly captures the topological natures and inter-point-cloud skeletal correlations with the noise-robust and density-invariant skeletal representations. Next, we introduce the Correspondence Dual-Sampler to facilitate correspondences by augmenting the correspondence set with skeletal correspondences. Furthermore, we construct a challenging novel cross-source point cloud dataset named KITTI CrossSource for benchmarking cross-source point cloud registration methods. Extensive quantitative and qualitative experiments are conducted to demonstrate our approach’s superiority and robustness on both cross-source and same-source datasets. To the best of our knowledge, our approach is the first to facilitate point cloud registration with skeletal geometric priors.

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NeurIPS Conference 2024 Conference Paper

The Dormant Neuron Phenomenon in Multi-Agent Reinforcement Learning Value Factorization

Haoyuan Qin
Chennan Ma
Mian Deng
Zhengzhu Liu
Songzhu Mei
Xinwang Liu
Cheng Wang
Siqi Shen

In this work, we study the dormant neuron phenomenon in multi-agent reinforcement learning value factorization, where the mixing network suffers from reduced network expressivity caused by an increasing number of inactive neurons. We demonstrate the presence of the dormant neuron phenomenon across multiple environments and algorithms, and show that this phenomenon negatively affects the learning process. We show that dormant neurons correlates with the existence of over-active neurons, which have large activation scores. To address the dormant neuron issue, we propose ReBorn, a simple but effective method that transfers the weights from over-active neurons to dormant neurons. We theoretically show that this method can ensure the learned action preferences are not forgotten after the weight-transferring procedure, which increases learning effectiveness. Our extensive experiments reveal that ReBorn achieves promising results across various environments and improves the performance of multiple popular value factorization approaches. The source code of ReBorn is available in \url{https: //github. com/xmu-rl-3dv/ReBorn}.

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NeurIPS Conference 2023 Conference Paper

E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learning

Xiuhong Lin
Changjie Qiu
Zhipeng Cai
Siqi Shen
Yu Zang
Weiquan Liu
Xuesheng Bian
Matthias Müller

Event cameras have emerged as a promising vision sensor in recent years due to their unparalleled temporal resolution and dynamic range. While registration of 2D RGB images to 3D point clouds is a long-standing problem in computer vision, no prior work studies 2D-3D registration for event cameras. To this end, we propose E2PNet, the first learning-based method for event-to-point cloud registration. The core of E2PNet is a novel feature representation network called Event-Points-to-Tensor (EP2T), which encodes event data into a 2D grid-shaped feature tensor. This grid-shaped feature enables matured RGB-based frameworks to be easily used for event-to-point cloud registration, without changing hyper-parameters and the training procedure. EP2T treats the event input as spatio-temporal point clouds. Unlike standard 3D learning architectures that treat all dimensions of point clouds equally, the novel sampling and information aggregation modules in EP2T are designed to handle the inhomogeneity of the spatial and temporal dimensions. Experiments on the MVSEC and VECtor datasets demonstrate the superiority of E2PNet over hand-crafted and other learning-based methods. Compared to RGB-based registration, E2PNet is more robust to extreme illumination or fast motion due to the use of event data. Beyond 2D-3D registration, we also show the potential of EP2T for other vision tasks such as flow estimation, event-to-image reconstruction and object recognition. The source code can be found at: https: //github. com/Xmu-qcj/E2PNet.

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AAAI Conference 2023 Conference Paper

FedGS: Federated Graph-Based Sampling with Arbitrary Client Availability

Zheng Wang
Xiaoliang Fan
Jianzhong Qi
Haibing Jin
Peizhen Yang
Siqi Shen
Cheng Wang

While federated learning has shown strong results in opti- mizing a machine learning model without direct access to the original data, its performance may be hindered by in- termittent client availability which slows down the conver- gence and biases the final learned model. There are significant challenges to achieve both stable and bias-free training un- der arbitrary client availability. To address these challenges, we propose a framework named Federated Graph-based Sam- pling (FEDGS), to stabilize the global model update and mitigate the long-term bias given arbitrary client availabil- ity simultaneously. First, we model the data correlations of clients with a Data-Distribution-Dependency Graph (3DG) that helps keep the sampled clients data apart from each other, which is theoretically shown to improve the approximation to the optimal model update. Second, constrained by the far- distance in data distribution of the sampled clients, we fur- ther minimize the variance of the numbers of times that the clients are sampled, to mitigate long-term bias. To validate the effectiveness of FEDGS, we conduct experiments on three datasets under a comprehensive set of seven client availability modes. Our experimental results confirm FEDGS’s advantage in both enabling a fair client-sampling scheme and improving the model performance under arbitrary client availability. Our code is available at https://github.com/WwZzz/FedGS.

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NeurIPS Conference 2023 Conference Paper

RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization

Siqi Shen
Chennan Ma
Chao Li
Weiquan Liu
Yongquan Fu
Songzhu Mei
Xinwang Liu
Cheng Wang

Multi-agent systems are characterized by environmental uncertainty, varying policies of agents, and partial observability, which result in significant risks. In the context of Multi-Agent Reinforcement Learning (MARL), learning coordinated and decentralized policies that are sensitive to risk is challenging. To formulate the coordination requirements in risk-sensitive MARL, we introduce the Risk-sensitive Individual-Global-Max (RIGM) principle as a generalization of the Individual-Global-Max (IGM) and Distributional IGM (DIGM) principles. This principle requires that the collection of risk-sensitive action selections of each agent should be equivalent to the risk-sensitive action selection of the central policy. Current MARL value factorization methods do not satisfy the RIGM principle for common risk metrics such as the Value at Risk (VaR) metric or distorted risk measurements. Therefore, we propose RiskQ to address this limitation, which models the joint return distribution by modeling quantiles of it as weighted quantile mixtures of per-agent return distribution utilities. RiskQ satisfies the RIGM principle for the VaR and distorted risk metrics. We show that RiskQ can obtain promising performance through extensive experiments. The source code of RiskQ is available in https: //github. com/xmu-rl-3dv/RiskQ.

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NeurIPS Conference 2022 Conference Paper

ResQ: A Residual Q Function-based Approach for Multi-Agent Reinforcement Learning Value Factorization

Siqi Shen
Mengwei Qiu
Jun Liu
Weiquan Liu
Yongquan Fu
Xinwang Liu
Cheng Wang

The factorization of state-action value functions for Multi-Agent Reinforcement Learning (MARL) is important. Existing studies are limited by their representation capability, sample efficiency, and approximation error. To address these challenges, we propose, ResQ, a MARL value function factorization method, which can find the optimal joint policy for any state-action value function through residual functions. ResQ masks some state-action value pairs from a joint state-action value function, which is transformed as the sum of a main function and a residual function. ResQ can be used with mean-value and stochastic-value RL. We theoretically show that ResQ can satisfy both the individual global max (IGM) and the distributional IGM principle without representation limitations. Through experiments on matrix games, the predator-prey, and StarCraft benchmarks, we show that ResQ can obtain better results than multiple expected/stochastic value factorization methods.

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