6 accepted papers
LiDAR-based place recognition (LPR) is a key component of visual localization and autonomous driving. Although LiDAR data are usually preprocessed by motion undistortion, which can greatly reduce scene distortion caused by sensor motion, 3-dimensional (3D) point clouds in complex scenes still show i
Partial label learning (PLL) is a complicated weakly supervised multi-classification task compounded by class imbalance. Currently, existing methods only rely on inter-class pseudo-labeling from inter-class features, often overlooking the significant impact of the intra-class imbalanced features com…
Fully test-time adaptation (FTTA) is to adapt a model trained on a source domain to a target domain during the testing phase. Traditional methods like entropy minimization primarily focus on reducing uncertainty in output predictions, yet often overlook the diversity in target prediction results, wh…
Fine-grained 3D segmentation is an important task in 3D object understanding, especially in applications such as intelligent manufacturing or parts analysis for 3D objects. However, many challenges involved in such problem are yet to be solved, such as i) interpreting the complex structures located…
Large-scale point cloud semantic understanding is an important problem in self-driving cars and autonomous robotics navigation. However, such problem involves many challenges, such as i) critical road objects (e.g., pedestrians, barriers) with diverse and varying input shapes; ii) distributed contex…
Recently, by employing the stacked extreme learning machine (ELM) based autoencoders (ELM-AE) and sparse AEs (SAE), multilayer ELM (ML-ELM) and hierarchical ELM (H-ELM) has been developed. Compared to the conventional stacked AEs, the ML-ELM and H-ELM usually achieve better generalization performanc…