Y Liu, K Zhu, G Wu, Y Ren, B Liu… - Proceedings of the …, 2023 - openaccess.thecvf.com
Reconstructing 3D vehicles from noisy and sparse partial point clouds is of great significance to autonomous driving. Most existing 3D reconstruction methods cannot be …
Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are …
Recovering full 3D shapes from partial observations is a challenging task that has been extensively addressed in the computer vision community. Many deep learning methods …
Z Wang, D Li, R Jiang - arXiv preprint arXiv:2410.04738, 2024 - arxiv.org
In recent years, 3D vision has become a crucial field within computer vision, powering a wide range of applications such as autonomous driving, robotics, augmented reality (AR) …
K Fu, Z Li, M Xu, X Luo, M Wang - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Learning-based rigid point cloud registration (RPCR) studies have made great progress recently but most existing methods have a small convergence region and can only be used …
P Shi, H Cheng, X Han, Y Zhou… - IEEE Robotics and …, 2023 - ieeexplore.ieee.org
Point cloud completion estimates complete shapes from incomplete point clouds to obtain higher-quality point cloud data. Most existing methods only consider global object features …
D classroom seating information is of great significance in a host of planning contexts including indoor navigation and facility management. Mapping classroom 3-D seats based …
R Liu, C Li, W Wan, J Pan… - IEEE Robotics and …, 2024 - ieeexplore.ieee.org
In this letter, we present a novel approach for planning an object's Next Best Views (NBV) so that a depth camera can collect the object's surface point cloud and reconstruct its 3D model …
The Gromov Wasserstein (GW) problem, a variant of the classical optimal transport (OT) problem, has attracted growing interest in the machine learning and data science …