PointDifformer: Robust Point Cloud Registration With Neural Diffusion and Transformer

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.

Autonomous Driving Point Cloud Registration

Image Patch-Matching with Graph-Based Learning in Street Scenes

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.

Autonomous Driving Metric Learning +1

RobustLoc: Robust Camera Pose Regression in Challenging Driving Environments

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.

Autonomous Driving camera absolute pose regression +3

Building Facade Parsing R-CNN

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.