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 …

The robustness of 3D perception systems under natural corruptions from environments and
sensors is pivotal for safety-critical applications. Existing large-scale 3D perception datasets …

Transformer, as an alternative to CNN, has been proven effective in many modalities (eg,
texts and images). For 3D point cloud transformers, existing efforts focus primarily on …

In recent years, significant achievements have been made in motion planning for intelligent
vehicles. However, as a typical unstructured environment, open-pit mining attracts limited …

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial
intelligence (AI), including computer vision, natural language processing, and speech …

Diffusion models have achieved great success in synthesizing high-quality images.
However generating high-resolution images with diffusion models is still challenging due to …

High-resolution images enable neural networks to learn richer visual representations.
However, this improved performance comes at the cost of growing computational …

We introduce SceneScript, a method that directly produces full scene models as a sequence
of structured language commands using an autoregressive, token-based approach. Our …

This study develops a unified Point Cloud Geometry (PCG) compression method through the
processing of multiscale sparse tensor-based voxelized PCG. We call this compression …

Extending the success of 2D Large Kernel to 3D perception is challenging due to: 1. the
cubically-increasing overhead in processing 3D data; 2. the optimization difficulties from …