6G network enables the rapid connection of autonomous vehicles, the generated internet of
vehicles establishes a large-scale point cloud, which requires automatic point cloud analysis …

Point cloud classification is a fundamental task in 3D applications. However, it is challenging
to achieve effective feature learning due to the irregularity and unordered nature of point …

We introduce FPConv, a novel surface-style convolution operator designed for 3D point
cloud analysis. Unlike previous methods, FPConv doesn't require transforming to …

Feature representation learning is a key component in 3D point cloud analysis. However,
the powerful convolutional neural networks (CNNs) cannot be applied due to the irregular …

Performances on standard 3D point cloud benchmarks have plateaued, resulting in
oversized models and complex network design to make a fractional improvement. We …

Despite great success of deep neural networks for 2D vision tasks, point clouds, unlike 2D
images, cannot be directly applied to traditional convolutional neural networks because of …

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 …

Abstract 3D point cloud understanding is an important component in autonomous driving
and robotics. In this paper, we present a novel Embedding-Querying paradigm (EQ …

It is a challenge to design a deep neural network for raw point cloud, which is disordered
and unstructured data. In this paper, we introduce a cross self-attention network (CSANet) to …

Depth sensors used in autonomous driving and gaming systems often report back 3D point
clouds. The lack of structure from these sensors does not allow these systems to take …