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Xihao Wang

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5 papers

2 author rows

AAAI Conference 2026 Conference Paper

Rethinking Surgical Smoke: A Smoke-Type-Aware Laparoscopic Video Desmoking Method and Dataset

Qifan Liang
Junlin Li
Zhen Han
Xihao Wang
Zhongyuan Wang
Bin Mei

Electrocautery or lasers will inevitably generate surgical smoke, which hinders the visual guidance of laparoscopic videos for surgical procedures. The surgical smoke can be classified into different types based on its motion patterns, leading to distinctive spatio-temporal characteristics across smoky laparoscopic videos. However, existing desmoking methods fail to account for such smoke-type-specific distinctions. Therefore, we propose the first Smoke-Type-Aware Laparoscopic Video Desmoking Network (STANet) by introducing two smoke types: Diffusion Smoke and Ambient Smoke. Specifically, a smoke mask segmentation sub-network is designed to jointly conduct smoke mask and smoke type predictions based on the attention-weighted mask aggregation, while a smokeless video reconstruction sub-network is proposed to perform specially desmoking on smoky features guided by two types of smoke mask. To address the entanglement challenges of two smoke types, we further embed a coarse-to-fine disentanglement module into the mask segmentation sub-network, which yields more accurate disentangled masks through the smoke-type-aware cross attention between non-entangled and entangled regions. In addition, we also construct the first large-scale synthetic video desmoking dataset with smoke type annotations. Extensive experiments demonstrate that our method not only outperforms state-of-the-art approaches in quality evaluations, but also exhibits superior generalization across multiple downstream surgical tasks.

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IROS Conference 2025 Conference Paper

Cross-Level Fusion: Integrating Object Lists with Raw Sensor Data for 3D Object Tracking

Xiangzhong Liu
Xihao Wang
Hao Shen

Smart sensors and Vehicle-To-Everything (V2X) modules are commonly utilized in automotive perception systems, which primarily provide processed object lists rather than raw data. However, high-level fusion approaches suffer from significant information loss and representational misalignment due to the inherently abstract and sparse nature of these high-level outputs. We propose a novel cross-level fusion paradigm that enables bidirectional information flow between object lists and raw vision features within an end-to-end Transformer framework for 3D object detection and tracking. Our approach extracts inherent positional and dimensional cues from object lists to generate two outputs: structured query features that are fused with the initial learnable queries in the Transformer decoder, and soft Gaussian attention masks that guide feature extraction. This integrated mechanism not only improves tracking accuracy by synergistically combining object priors with fine-grained vision data but also promotes hardware economy and AI model sustainability by adapting legacy sensors to evolving sensor setups. To overcome the lack of dedicated datasets, we develop a pseudo object list generation pipeline that simulates realistic sensor tracking behavior. Experiments on the nuScenes dataset demonstrate significant performance gains over vision-only baselines and robust generalization across diverse noise levels, validating the efficacy of our cross-level fusion strategy. The code is available at: https://github.com/CesarLiu/DNF.git.

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ICRA Conference 2024 Conference Paper

ProEqBEV: Product Group Equivariant BEV Network for 3D Object Detection in Road Scenes of Autonomous Driving

Hongwei Liu
Jian Yang 0034
Zhengyu Li
Ke Li 0005
Jianzhang Zheng
Xihao Wang
Xuan Tang
Mingsong Chen 0001

With the rapid development of autonomous driving systems, 3D object detection based on Bird’s Eye View (BEV) in road scenes has witnessed great progress over the past few years. As a road scene exhibits a part-whole hierarchy between the within objects and the scene itself, simple parts (e. g. , roads, lane lines, vehicles and pedestrians) can be assembled into progressively more complex shapes to form a BEV representation of the whole road scene. Therefore, a BEV often has multiple levels of freedom on motion, i. e. , the rotation and the moving shift of the whole BEV, and the random movements of objects (e. g. , pedestrians and vehicles) inside the BEV. However, most of the current single-sensor or multi-sensor fusion-based BEV object detection methods have not yet taken into account capturing such multi-level motion in a BEV. To address this problem, we propose a product group equivariant object detection network framework that is equivariant with respect to multiple levels of symmetry groups based on multi-sensor fusion. The proposed framework extracts local equivariant features of objects in point clouds, while global equivariant features are extracted in both point clouds and images. Furthermore, the network learns diverse rotation-equivariant features and mitigates a significant amount of detection errors caused by rotations of BEV and objects inside a BEV, thereby further enhancing the performance of object detection. The experiment results show that the network architecture significantly improves object detection on mAP and NDS, respectively. In addition, in order to demonstrate the effectiveness of the proposed local-multi-global equivariant components, we conduct sufficient ablation experiments. The results show that the individual components are indispensable for the object detection performance improvement of the overall network architecture.

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ICRA Conference 2023 Conference Paper

DuEqNet: Dual-Equivariance Network in Outdoor 3D Object Detection for Autonomous Driving

Xihao Wang
JiaMing Lei
Hai Lan
Arafat Al-Jawari
Xian Wei

Outdoor 3D object detection has played an essential role in the environment perception of autonomous driving. In complicated traffic situations, precise object recognition provides indispensable information for prediction and planning in the dynamic system, improving self-driving safety and reliability. However, with the vehicle's veering, the constant rotation of the surrounding scenario makes a challenge for the perception systems. Yet most existing methods have not focused on alleviating the detection accuracy impairment brought by the vehicle's rotation, especially in outdoor 3D detection. In this paper, we propose DuEqNet, which first introduces the concept of equivariance into 3D object detection network by leveraging a hierarchical embedded framework. The dual-equivariance of our model can extract the equivariant features at both local and global levels, respectively. For the local feature, we utilize the graph-based strategy to guarantee the equivariance of the feature in point cloud pillars. In terms of the global feature, the group equivariant convolution layers are adopted to aggregate the local feature to achieve the global equivariance. In the experiment part, we evaluate our approach with different baselines in 3D object detection tasks and obtain State-Of-The-Art performance. According to the results, our model presents higher accuracy on orientation and better prediction efficiency. Moreover, our dual-equivariance strategy exhibits the satisfied plug-and-play ability on various popular object detection frameworks to improve their performance.

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NeurIPS Conference 2022 Conference Paper

Geodesic Self-Attention for 3D Point Clouds

Zhengyu Li
Xuan Tang
Zihao Xu
Xihao Wang
Hui Yu
Mingsong Chen
Xian Wei

Due to the outstanding competence in capturing long-range relationships, self-attention mechanism has achieved remarkable progress in point cloud tasks. Nevertheless, point cloud object often has complex non-Euclidean spatial structures, with the behavior changing dynamically and unpredictably. Most current self-attention modules highly rely on the dot product multiplication in Euclidean space, which cannot capture internal non-Euclidean structures of point cloud objects, especially the long-range relationships along the curve of the implicit manifold surface represented by point cloud objects. To address this problem, in this paper, we introduce a novel metric on the Riemannian manifold to capture the long-range geometrical dependencies of point cloud objects to replace traditional self-attention modules, namely, the Geodesic Self-Attention (GSA) module. Our approach achieves state-of-the-art performance compared to point cloud Transformers on object classification, few-shot classification and part segmentation benchmarks.

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