The indoor scene object detection technology is of important research significance, which is
one of the popular research topics in the field of scene understanding for indoor robots. In …

Point cloud segmentation is an essential task in three-dimensional (3D) vision and
intelligence. It is a critical step in understanding 3D scenes with a variety of applications …

With the advancement of 3D sensing technologies, point clouds are gradually becoming the
main type of data representation in applications such as autonomous driving, robotics, and …

Point cloud is usually used to construct the surface shape of three-dimensional geometric
objects. Due to the disorder and irregularity of the point cloud, it is still a challenge to fully …

Adversarial attacks on point clouds play a vital role in assessing and enhancing the
adversarial robustness of 3D deep learning models. While employing a variety of geometric …

For large-scale point cloud semantic segmentation, the relationships between long-range
neighbourhoods are as important as short-range features. The current methods focus on …

In indoor scenes, due to the various semantic categories and scale differences of objects,
the complex spatial structure of objects, and the different reflection characteristics of laser …

Designing efficient deep learning models for 3D point clouds is an important research topic.
Point-voxel convolution (Liu et al. in NeurIPS, 2019) is a pioneering approach in this …

Adversarial attacks on point clouds are crucial for assessing and improving the adversarial
robustness of 3D deep learning models. Despite leveraging various geometric constraints …

We introduce a multimodal feature fusion network (MFFNet) for 3D point cloud semantic
segmentation. Unlike previous methods that directly learn from colored point clouds …