Point cloud data have been widely explored due to its superior accuracy and robustness
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …

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 …

This paper is not motivated to seek innovation within the attention mechanism. Instead it
focuses on overcoming the existing trade-offs between accuracy and efficiency within the …

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 …

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 …

We introduce OpenShape, a method for learning multi-modal joint representations of text,
image, and point clouds. We adopt the commonly used multi-modal contrastive learning …

The rapid advancement of deep learning models is often attributed to their ability to leverage
massive training data. In contrast such privilege has not yet fully benefited 3D deep learning …

The rapid progress in 3D scene understanding has come with growing demand for data;
however, collecting and annotating 3D scenes (eg point clouds) are notoriously hard. For …

Segmenting or detecting objects in sparse Lidar point clouds are two important tasks in
autonomous driving to allow a vehicle to act safely in its 3D environment. The best …

Pan-sharpening aims to integrate the complementary information of texture-rich PAN images
and multi-spectral (MS) images to produce the texture-rich MS images. Despite the …