Author name cluster

Xin Ouyang

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4 papers

2 author rows

ICRA Conference 2025 Conference Paper

MDC-Seg: Multi-Directional Convolution-Based Semantic Segmentation for LiDAR Point Clouds

Xin Ouyang
Xiaolong Qian
Yunzhou Zhang
You Shen
Guiyuan Wang
Wei Liu 0022

LiDAR point clouds 3D semantic segmentation enables efficient and accurate environmental sensing for intelligent vehicles and autonomous robots, greatly advancing these domains. Existing advanced methods that use 3D sparse convolutional often suffer from a small Effective Receptive Field (ERF), which limits context sensing and challenging highperformance segmentation. Building on this observation, we propose MDC-Seg for efficient ERF enlargement. We design Multi-directional Convolution (MDConv), which simultaneously performs sparse feature encoding on the Bird's Eye View (BEV) and Range View (RV) planes to enlarge the ERF of 3D sparse convolution. To enhance feature fusion in MDConv, we introduce an attention mechanism and design an efficient multifeature fusion (EMFF) module suitable for both 3D and 2D sparse features. To improve segmentation accuracy, we design a point-voxel constraint (PVC) module to handle edge voxels containing multiple point cloud categories, optimizing the final inference results. These modules add minimal memory and inference time but significantly improve performance compared to the baseline. Extensive experiments on the SemanticKITTI benchmark demonstrate MDC-Seg's excellent performance, with supplementary tests on nuScenes further confirming its superiority by yielding good results. The source code is available at https://github.com/OYgreat-river/MDC-Seg.

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EAAI Journal 2024 Journal Article

Electric bikes charging anomaly detection from alternating current side based on big data

Fang Yang
Yang Yang
Xu Wang
Xin Ouyang
Chunyan Shuai

With the widespread use of electric bikes (E-bikes), charging safety incidents occur frequently, even causing serious hazards. However, detecting and warning of unsafe charging from the E-bikes side is a challenge due to the lack of full-featured battery management systems and communication means vehicles and chargers in majority E-bikes. Aim for this, a diagnosis scheme is proposed to detect E-bikes’ abnormal charging from the alternating current (AC) side of the charging pile. Initially, 91, 282 charging records are collected from charging piles to analyze the correlations between the current features and the battery working principle, charging mode, and user behavior in depth. Then, ten current features and six feature sequences are formulated, and two algorithms based on the first-order difference and pattern-matching are proposed to recognize and extracted these features and feature sequences. A feature-based random forest model is presented to identify the abnormal charging. Empirical studies show that the anomaly recognition performance of the proposed framework exceeds that of the baselines, achieving a recognition precision of 0. 89 and an F1-score of 0. 86. The application of this scheme can provide early warning for unsafe charging from the charging pile side without modification of the existing E-bikes, and can be extended to diagnose the charging safety of other battery-powered system, such as electric vehicles. Meanwhile, the analysis of internal and external factors that lead to abnormal charging is beneficial for charging operation companies and government to develop charging security specifications and regulate charging behaviors.

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AAAI Conference 2024 Conference Paper

LLMRG: Improving Recommendations through Large Language Model Reasoning Graphs

Yan Wang
Zhixuan Chu
Xin Ouyang
Simeng Wang
Hongyan Hao
Yue Shen
Jinjie Gu
Siqiao Xue

Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behaviors and profiles. In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs. These graphs link a user's profile and behavioral sequences through causal and logical inferences, representing the user's interests in an interpretable way. Our approach, LLM reasoning graphs (LLMRG), has four components: chained graph reasoning, divergent extension, self-verification and scoring, and knowledge base self-improvement. The resulting reasoning graph is encoded using graph neural networks, which serves as additional input to improve conventional recommender systems, without requiring extra user or item information. Our approach demonstrates how LLMs can enable more logical and interpretable recommender systems through personalized reasoning graphs. LLMRG allows recommendations to benefit from both engineered recommendation systems and LLM-derived reasoning graphs. We demonstrate the effectiveness of LLMRG on benchmarks and real-world scenarios in enhancing base recommendation models.

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ICRA Conference 2024 Conference Paper

VPE-SLAM: Neural Implicit Voxel-permutohedral Encoding for SLAM

Zhiyao Zhang
Yunzhou Zhang
You Shen
Lei Rong
Sizhan Wang
Xin Ouyang
Yulong Li

NeRF can reconstruct incredibly realistic environmental maps in dense simultaneous localization and mapping, providing robots with more comprehensive scene map information. However, NeRF often struggles with geometric distortions in indoor reconstructions. To correct geometric distortions, we develop VPE-SLAM, based on the proposed voxel-permutohedral encoding, which can incrementally reconstruct maps of unknown scenes. Specifically, voxel-permutohedral encoding combines a sparse voxel feature grid created by an octree and multi-resolution permutohedral tetrahedral feature grids to represent the scene effectively. Especially when dealing with object edges, our method can effectively encode the geometry and texture of edges by the hybrid structural grid. We propose a novel local bundle adjustment module that utilizes a sliding window mechanism to manage adjacent keyframes requiring optimization. Furthermore, the proposed method establishes local map consistency by repeatedly optimizing keyframes that were initially under-optimized through a compensation strategy. The consistency of the local map can enhance the adaptability of our method to challenging scenes. Extensive experiments demonstrate that our method can achieve accurate camera tracking and produce high-quality reconstruction results on the Replica and ScanNet datasets. The source code will be available at https://github.com/NeuCV-IRMI/VPE-SLAM.

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