In the past year, MultiModal Large Language Models (MM-LLMs) have undergone
substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs …

In recent years, point cloud representation has become one of the research hotspots in the
field of computer vision, and has been widely used in many fields, such as autonomous …

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 survey applications of pretrained foundation models in robotics. Traditional deep
learning models in robotics are trained on small datasets tailored for specific tasks, which …

The recognition capabilities of current state-of-the-art 3D models are limited by datasets with
a small number of annotated data and a pre-defined set of categories. In its 2D counterpart …

The unprecedented advancements in Large Language Models (LLMs) have shown a
profound impact on natural language processing but are yet to fully embrace the realm of 3D …

PointNet++ is one of the most influential neural architectures for point cloud understanding.
Although the accuracy of PointNet++ has been largely surpassed by recent networks such …

We introduce MeshGPT a new approach for generating triangle meshes that reflects the
compactness typical of artist-created meshes in contrast to dense triangle meshes extracted …

Being data-driven is one of the most iconic properties of deep learning algorithms. The birth
of ImageNet drives a remarkable trend of" learning from large-scale data" in computer vision …

Mainstream 3D representation learning approaches are built upon contrastive or generative
modeling pretext tasks, where great improvements in performance on various downstream …