Image semantic segmentation is more and more being of interest for computer vision and
machine learning researchers. Many applications on the rise need accurate and efficient …

A point cloud is a set of points defined in a 3D metric space. Point clouds have become one
of the most significant data formats for 3D representation and are gaining increased …

PointNet++ is one of the most influential neural architectures for point cloud understanding.
Although the accuracy of PointNet++ has been largely surpassed by recent networks such …

As a promising scheme of self-supervised learning, masked autoencoding has significantly
advanced natural language processing and computer vision. Inspired by this, we propose a …

We present Point-BERT, a novel paradigm for learning Transformers to generalize the
concept of BERT onto 3D point cloud. Following BERT, we devise a Masked Point Modeling …

Masked Autoencoders (MAE) have shown great potentials in self-supervised pre-training for
language and 2D image transformers. However, it still remains an open question on how to …

Pre-training by numerous image data has become de-facto for robust 2D representations. In
contrast, due to the expensive data processing, a paucity of 3D datasets severely hinders …

Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object
classification, segmentation and detection is often laborious owing to the irregular structure …

Point cloud analysis is challenging due to irregularity and unordered data structure. To
capture the 3D geometries, prior works mainly rely on exploring sophisticated local …

Mainstream 3D representation learning approaches are built upon contrastive or generative
modeling pretext tasks, where great improvements in performance on various downstream …