Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

Abstract Three-dimensional (3D) object recognition is widely used in automated driving,
medical image analysis, virtual/augmented reality, artificial intelligence robots, and other …

Large-scale pre-trained models have shown promising open-world performance for both
vision and language tasks. However, their transferred capacity on 3D point clouds is still …

Multiview clustering partitions data into different groups according to their heterogeneous
features. Most existing methods degenerate the applicability of models due to their …

This paper presents SO-Net, a permutation invariant architecture for deep learning with
orderless point clouds. The SO-Net models the spatial distribution of point cloud by building …

View-based approach that recognizes 3D shape through its projected 2D images has
achieved state-of-the-art results for 3D shape recognition. The major challenge for view …

Exploring contextual information in the local region is important for shape understanding
and analysis. Existing studies often employ hand-crafted or explicit ways to encode …

Feature learning on point clouds has shown great promise, with the introduction of effective
and generalizable deep learning frameworks such as pointnet++. Thus far, however, point …

Point cloud is an important type of 3D representation. However, directly applying
convolutions on point clouds is challenging due to the sparse, irregular and unordered data …

View-based methods have achieved considerable success in $3 $ D object recognition
tasks. Different from existing view-based methods pooling the view-wise features, we tackle …