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Eryun Liu

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

SDNet: LiDAR Semantic Scene Completion with Sparse-Dense Fusion and Input-Aware Label Refinement

Tingming Bai
Zhiyu Xiang
Peng Xu
Tianyu Pu
Kai Wang
Eryun Liu

LiDAR Semantic Scene Completion (SSC) in autonomous driving requires predicting both dense occupancy and semantic labels from sparse input point cloud. Existing methods typically adopt cascaded architecture for feature dilation and semantic abstraction, which blurs distinctive geometric patterns and reduces feature discriminability. Moreover, given an input, conventional processing of the ground truth labels overlooks voxel predictability in the target, resulting in ill-posed supervision and discards informative voxels. To address these limitations, we propose Sparse-Dense Net (SDNet), a dual-branch architecture that processes the input points through parallel sparse and dense encoders. The complementary features are aligned and fused using a Sparse Dense Feature Fusion (SDFF) module and further refined by a Feature Propagation (FP) module. Additionally, we introduce an input-aware label refinement strategy, including Sparse-Guided Filtering (SGF) to filter unpredictable targets and Ignored Voxel Recycling (IVR) to leverage informative ignored voxels for auxiliary supervision. These innovations enhance both feature learning and label quality. Extensive experiments on SemanticKITTI and nuScenes OpenOccupancy datasets validate the effectiveness of our approach, with SDNet achieving state-of-the-art performance on both datasets and ranking 1st on the official SemanticKITTI benchmark with 42.1 mIoU, outperforming the previous best by 4.2 (+11.1\%).

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ECAI Conference 2025 Conference Paper

Multivariate Time Series Forecasting with Hybrid Euclidean-SPD Manifold Graph Neural Networks

Yong Fang 0001
Na Li
Hangguan Shan
Eryun Liu
Xinyu Li 0001
Wei Ni
Erping Li 0001

Multivariate Time Series (MTS) forecasting plays a vital role in various real-world applications, such as traffic management and predictive maintenance. Existing approaches typically model MTS data in either Euclidean or Riemannian space, limiting their ability to capture the diverse geometric structures and complex Spatio-Temporal (ST) dependencies inherent in real-world data. To overcome this limitation, we propose the Hybird Symmetric Positive-Definite Manifold Graph Neural Network (HSMGNN), a novel graph neural network-based model that captures data geometry within a hybrid Euclidean–Riemannian framework. To the best of our knowledge, this is the first work to leverage hybrid geometric representations for MTS forecasting, enabling expressive and comprehensive modeling of geometric properties. Specifically, we introduce a Submanifold-Cross-Segment (SCS) embedding to project input MTS into both Euclidean and Riemannian spaces, thereby capturing ST variations across distinct geometric domains. To alleviate the high computational cost of Riemannian distance, we further design an Adaptive-Distance-Bank (ADB) layer with a trainable memory mechanism. Finally, a Fusion Graph Convolutional Network (FGCN) is devised to integrate features from the dual spaces via a learnable fusion operator for accurate prediction. Experiments on three benchmark datasets demonstrate that HSMGNN achieves up to 13. 8% improvement over state-of-the-art baselines in forecasting accuracy.

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AAAI Conference 2025 Conference Paper

Privacy-Preserving V2X Collaborative Perception Integrating Unknown Collaborators

Bin Lu
Xinyu Xiao
Changzhou Zhang
Yang Zhou
Zhiyu Xiang
Hangguan Shan
Eryun Liu

Vehicle-to-everything (V2X) collaborative perception has recently gained increasing attention in autonomous driving due to its ability to enhance scene understanding by integrating information from other collaborators, e.g. vehicles or infrastructure. Existing algorithms usually share deep features to achieve a trade-off between accuracy and bandwidth. However, most of these methods require joint training of all agents, which results in privacy leakage and is impractical and unacceptable in the real world. Sharing prediction results seems to be a direct solution, but its performance is suboptimal and sensitive to localization noise and communication delay. In this paper, we propose a privacy-preserving collaborative perception framework, where each agent is separately trained with its own dataset and the ego vehicle needs to integrate with completely unknown collaborators. Specifically, we propose MSD, a multi-scale feature fusion method combined with deformable attention, to better fuse features of different agents. We also propose a plug-in domain adapter to align the features from unknown collaborators to ego-domain. Extensive experiments on the challenging DAIR-V2X and V2V4Real demonstrate that: 1) MSD achieves remarkable performance, outperforming others by at least 2.8% and 6.7% in AP0.7 on DAIR-V2X and V2V4Real, respectively; 2) After domain adaptation, it significantly outperforms the No Fusion, Late Fusion scenarios and can approach or even surpass the performance of joint training. We truly achieves privacy-preserving collaboration, providing a new paradigm for the study of collaborative perception, which is crucial for practical applications.

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NeurIPS Conference 2025 Conference Paper

RayFusion: Ray Fusion Enhanced Collaborative Visual Perception

Shaohong Wang
Lu Bin
Xinyu Xiao
Hanzhi Zhong
Bowen Pang
Tong Wang
Zhiyu Xiang
Hangguan Shan

Collaborative visual perception methods have gained widespread attention in the autonomous driving community in recent years due to their ability to address sensor limitation problems. However, the absence of explicit depth information often makes it difficult for camera-based perception systems, e. g. , 3D object detection, to generate accurate predictions. To alleviate the ambiguity in depth estimation, we propose RayFusion, a ray-based fusion method for collaborative visual perception. Using ray occupancy information from collaborators, RayFusion reduces redundancy and false positive predictions along camera rays, enhancing the detection performance of purely camera-based collaborative perception systems. Comprehensive experiments show that our method consistently outperforms existing state-of-the-art models, substantially advancing the performance of collaborative visual perception. Our code will be made publicly available.

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AAAI Conference 2025 Conference Paper

SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object Detection

Ruoyu Xu
Zhiyu Xiang
Chenwei Zhang
Hanzhi Zhong
Xijun Zhao
Ruina Dang
Peng Xu
Tianyu Pu

3D object detection is one of the fundamental perception tasks for autonomous vehicles. Fulfilling such a task with a 4D millimeter-wave radar is very attractive since the sensor is able to acquire 3D point clouds similar to Lidar while maintaining robust measurements under adverse weather. However, due to the high sparsity and noise associated with the radar point clouds, the performance of the existing methods is still much lower than expected. In this paper, we propose a novel Semi-supervised Cross-modality Knowledge Distillation (SCKD) method for 4D radar-based 3D object detection. It characterizes the capability of learning the feature from a Lidar-radar-fused teacher network with semi-supervised distillation. We first propose an adaptive fusion module in the teacher network to boost its performance. Then, two feature distillation modules are designed to facilitate the cross-modality knowledge transfer. Finally, a semi-supervised output distillation is proposed to increase the effectiveness and flexibility of the distillation framework. With the same network structure, our radar-only student trained by SCKD boosts the mAP by 10.38% over the baseline and outperforms the state-of-the-art works on the VoD dataset. The experiment on ZJUODset also shows 5.12% mAP improvements on the moderate difficulty level over the baseline when extra unlabeled data are available.

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AAAI Conference 2024 Conference Paper

Exploring Base-Class Suppression with Prior Guidance for Bias-Free One-Shot Object Detection

Wenwen Zhang
Yun Hu
Hangguan Shan
Eryun Liu

One-shot object detection (OSOD) aims to detect all object instances towards the given category specified by a query image. Most existing studies in OSOD endeavor to establish effective cross-image correlation with limited query information, however, ignoring the problems of the model bias towards the base classes and the generalization degradation on the novel classes. Observing this, we propose a novel algorithm, namely Base-class Suppression with Prior Guidance (BSPG) network to achieve bias-free OSOD. Specifically, the objects of base categories can be detected by a base-class predictor and eliminated by a base-class suppression module (BcS). Moreover, a prior guidance module (PG) is designed to calculate the correlation of high-level features in a non-parametric manner, producing a class-agnostic prior map with unbiased semantic information to guide the subsequent detection process. Equipped with the proposed two modules, we endow the model with a strong discriminative ability to distinguish the target objects from distractors belonging to the base classes. Extensive experiments show that our method outperforms the previous techniques by a large margin and achieves new state-of-the-art performance under various evaluation settings.

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IROS Conference 2023 Conference Paper

A Two-Stage Based Social Preference Recognition in Multi-Agent Autonomous Driving System

Jintao Xue
Dongkun Zhang
Rong Xiong
Yue Wang 0020
Eryun Liu

Multi-Agent Reinforcement Learning (MARL) has become a promising solution for constructing a multi-agent autonomous driving system (MADS) in complex and dense scenarios. But most methods consider agents acting selfishly, which leads to conflict behaviors. Some existing works incorporate the concept of social value orientation (SVO) to promote coordination, but they lack the knowledge of other agents' SVOs, resulting in conservative maneuvers. In this paper, we aim to tackle the mentioned problem by enabling the agents to understand other agents' SVOs. To accomplish this, we propose a two-stage system framework. Firstly, we train a policy by allowing the agents to share their ground truth SVOs to establish a coordinated traffic flow. Secondly, we develop a recognition network that estimates agents' SVOs and integrates it with the policy trained in the first stage. Experiments demonstrate that our developed method significantly improves the performance of the driving policy in MADS compared to two state-of-the-art MARL algorithms.

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