no code implementations • 22 Apr 2024 • Rui She, Qiyu Kang, Sijie Wang, Wee Peng Tay, Kai Zhao, Yang song, Tianyu Geng, Yi Xu, Diego Navarro Navarro, Andreas Hartmannsgruber
Point cloud registration is a fundamental technique in 3-D computer vision with applications in graphics, autonomous driving, and robotics.
no code implementations • 9 Jan 2024 • Qiyu Kang, Kai Zhao, Yang song, Yihang Xie, Yanan Zhao, Sijie Wang, Rui She, Wee Peng Tay
In this work, we rigorously investigate the robustness of graph neural fractional-order differential equation (FDE) models.
no code implementations • 6 Jan 2024 • Rui She, Sijie Wang, Qiyu Kang, Kai Zhao, Yang song, Wee Peng Tay, Tianyu Geng, Xingchao Jian
We leverage a graph neural partial differential equation (PDE) based on Beltrami flow to obtain high-dimensional features and position embeddings for point clouds.
1 code implementation • 17 Dec 2023 • Sijie Wang, Rui She, Qiyu Kang, Xingchao Jian, Kai Zhao, Yang song, Wee Peng Tay
The utilization of multi-modal sensor data in visual place recognition (VPR) has demonstrated enhanced performance compared to single-modal counterparts.
no code implementations • 8 Nov 2023 • Rui She, Qiyu Kang, Sijie Wang, Wee Peng Tay, Yong Liang Guan, Diego Navarro Navarro, Andreas Hartmannsgruber
Matching landmark patches from a real-time image captured by an on-vehicle camera with landmark patches in an image database plays an important role in various computer perception tasks for autonomous driving.
1 code implementation • 7 Nov 2023 • Rui She, Qiyu Kang, Sijie Wang, Yuan-Rui Yang, Kai Zhao, Yang song, Wee Peng Tay
For autonomous vehicles (AVs), visual perception techniques based on sensors like cameras play crucial roles in information acquisition and processing.
no code implementations • 15 Oct 2023 • Zeyu Zhang, Shuyan Wan, Sijie Wang, Xianda Zheng, Xinrui Zhang, Kaiqi Zhao, Jiamou Liu, Dong Hao
Signed Graph Neural Networks (SGNNs) are vital for analyzing complex patterns in real-world signed graphs containing positive and negative links.
1 code implementation • NeurIPS 2023 • Kai Zhao, Qiyu Kang, Yang song, Rui She, Sijie Wang, Wee Peng Tay
Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those that affect both node features and graph topology.
no code implementations • 23 Sep 2023 • Lin Ni, Sijie Wang, Zeyu Zhang, Xiaoxuan Li, Xianda Zheng, Paul Denny, Jiamou Liu
Learnersourcing offers great potential for scalable education through student content creation.
1 code implementation • 30 May 2023 • Qiyu Kang, Kai Zhao, Yang song, Sijie Wang, Wee Peng Tay
In the graph node embedding problem, embedding spaces can vary significantly for different data types, leading to the need for different GNN model types.
1 code implementation • 26 May 2023 • Kai Zhao, Qiyu Kang, Yang song, Rui She, Sijie Wang, Wee Peng Tay
Graph neural networks (GNNs) have shown promising results across various graph learning tasks, but they often assume homophily, which can result in poor performance on heterophilic graphs.
2 code implementations • CVPR 2023 • Sijie Wang, Qiyu Kang, Rui She, Wei Wang, Kai Zhao, Yang song, Wee Peng Tay
LiDAR relocalization plays a crucial role in many fields, including robotics, autonomous driving, and computer vision.
no code implementations • 2 Mar 2023 • Qiyu Kang, Kai Zhao, Yang song, Sijie Wang, Rui She, Wee Peng Tay
Graph neural networks (GNNs) have achieved success in various inference tasks on graph-structured data.
1 code implementation • 21 Nov 2022 • Sijie Wang, Qiyu Kang, Rui She, Wee Peng Tay, Andreas Hartmannsgruber, Diego Navarro Navarro
Experiments demonstrate that RobustLoc surpasses current state-of-the-art camera pose regression models and achieves robust performance in various environments.
Ranked #1 on Visual Localization on Oxford RobotCar Full
1 code implementation • 16 Sep 2022 • Yang song, Qiyu Kang, Sijie Wang, Zhao Kai, Wee Peng Tay
In this work, we explore the robustness properties of graph neural PDEs.
1 code implementation • 12 May 2022 • Sijie Wang, Qiyu Kang, Rui She, Wee Peng Tay, Diego Navarro Navarro, Andreas Hartmannsgruber
Building facade parsing, which predicts pixel-level labels for building facades, has applications in computer vision perception for autonomous vehicle (AV) driving.