6 papers
Retrieving Objects from 3D Scenes with Box-Guided Open-Vocabulary Instance Segmentation
Khanh Nguyen, Dasith de Silva Edirimuni, Ghulam Mubashar Hassan +1
Locating and retrieving objects from scene-level point clouds is a challenging problem with broad applications in robotics and augmented reality. This task is commonly formulated a…
LiDAR, GNSS and IMU Sensor Fine Alignment through Dynamic Time Warping to Construct 3D City Maps
Haitian Wang, Hezam Albaqami, Xinyu Wang +5
LiDAR-based 3D mapping suffers from cumulative drift causing global misalignment, particularly in GNSS-constrained environments. To address this, we propose a unified framework tha…
Geo-Registration of Terrestrial LiDAR Point Clouds with Satellite Images without GNSS
Xinyu Wang, Muhammad Ibrahim, Haitian Wang +3
Accurate geo-registration of LiDAR point clouds remains a significant challenge in urban environments where Global Navigation Satellite System (GNSS) signals are denied or degraded…
Occlusion-aware Text-Image-Point Cloud Pretraining for Open-World 3D Object Recognition
Khanh Nguyen, Ghulam Mubashar Hassan, Ajmal Mian
Recent open-world representation learning approaches have leveraged CLIP to enable zero-shot 3D object recognition. However, performance on real point clouds with occlusions still…
Multispectral Remote Sensing for Weed Detection in West Australian Agricultural Lands
Haitian Wang, Muhammad Ibrahim, Yumeng Miao +3
The Kondinin region in Western Australia faces significant agricultural challenges due to pervasive weed infestations, causing economic losses and ecological impacts. This study co…
Automated Road Extraction and Centreline Fitting in LiDAR Point Clouds
Xinyu Wang, Muhammad Ibrahim, Atif Mansoor +2
Road information extraction from 3D point clouds is useful for urban planning and traffic management. Existing methods often rely on local features and the refraction angle of lase…