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 …

During image editing, existing deep generative models tend to re-synthesize the entire
output from scratch, including the unedited regions. This leads to a significant waste of …

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

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 …

Generative artificial intelligence (GenAI) has brought about profound transformations across
the diverse domains of the Internet of Things such as manufacturing, marketing, medicine …

Diffusion models (DMs) excel in photo-realistic image synthesis but their adaptation to
LiDAR scene generation poses a substantial hurdle. This is primarily because DMs …

We present XCube a novel generative model for high-resolution sparse 3D voxel grids with
arbitrary attributes. Our model can generate millions of voxels with a finest effective …

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 …

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

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