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 …

In the context of autonomous driving the significance of effective feature learning is widely
acknowledged. While conventional 3D self-supervised pre-training methods have shown …

The booming of 3D recognition in the 2020s began with the introduction of point cloud
transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models …

Self-supervised 3D representation learning aims to learn effective representations from
large-scale unlabeled point clouds. Most existing approaches adopt point discrimination as …

In-context learning provides a new perspective for multi-task modeling for vision and NLP.
Under this setting the model can perceive tasks from prompts and accomplish them without …

In contrast to numerous NLP and 2D computer vision foundational models, the learning of a
robust and highly generalized 3D foundational model poses considerably greater …

A unified and versatile LiDAR segmentation model with strong robustness and
generalizability is desirable for safe autonomous driving perception. This work presents …

Realizing unified monocular 3D object detection including both indoor and outdoor scenes
holds great importance in applications like robot navigation. However involving various …

Vision-language pre-training (VLP) aims to learn joint representations of vision and
language modalities. The contrastive paradigm is currently dominant in this field. However …

Recently, graph-based and Transformer-based deep learning have demonstrated excellent
performances on various point cloud tasks. Most of the existing graph-based methods rely …