Abstract 3D point clouds are a crucial type of data collected by LiDAR sensors and widely
used in transportation applications due to its concise descriptions and accurate localization …

With the growing availability of extensive 3D datasets and the rapid progress in
computational power, deep learning (DL) has emerged as a highly promising approach for …

Prior to the deep learning era, shape was commonly used to describe the objects.
Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly …

Point cloud data has been extensively studied due to its compact form and flexibility in
representing complex 3D structures. The ability of point cloud data to accurately capture and …

Masked Modeling (MM) has demonstrated widespread success in various vision challenges,
by reconstructing masked visual patches. Yet, applying MM for large-scale 3D scenes …

Masked Autoencoders (MAE) have demonstrated promising performance in self-supervised
learning for both 2D and 3D computer vision. Nevertheless, existing MAE-based methods …

Masked autoencoders (MAE) have recently been introduced to 3D self-supervised
pretraining for point clouds due to their great success in NLP and computer vision. Unlike …

Semantic analyses of object point clouds are largely driven by releasing of benchmarking
datasets, including synthetic ones whose instances are sampled from object CAD models …

Masked autoencoding has gained momentum for improving fine-tuning performance in
many downstream tasks. However, it tends to focus on low-level reconstruction details …

Masked point modeling (MPM) has gained considerable attention in self‐supervised
learning for 3D point clouds. While existing self‐supervised methods have progressed in …