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 …

The rapid advancement of deep learning models is often attributed to their ability to leverage
massive training data. In contrast such privilege has not yet fully benefited 3D deep learning …

Recently, transformer-based methods have dominated 3D instance segmentation, where
mask attention is commonly involved. Specifically, object queries are guided by the initial …

The use of pretrained backbones with fine-tuning has been successful for 2D vision and
natural language processing tasks, showing advantages over task-specific networks. In this …

In recent years, 3D point clouds (PCs) have gained significant attention due to their diverse
applications across various fields, such as computer vision (CV), condition monitoring (CM) …

Aiming at obtaining structural information and 3D motion of dynamic scenes, scene flow
estimation has been an interest of research in computer vision and computer graphics for a …

The test-time optimization of scene flow—using a coordinate network as a neural prior [27]—
has gained popularity due to its simplicity, lack of dataset bias, and state-of-the-art …

Three-dimensional laser scanning technology is widely employed in various fields due to its
advantage in rapid acquisition of geographic scene structures. Achieving high precision and …

In the field of deep point cloud understanding KPConv is a unique architecture that uses
kernel points to locate convolutional weights in space instead of relying on Multi-Layer …

In recent years, the Transformer model has made significant progress in semantic
segmentation tasks. However, existing self-attention mechanisms perform well in capturing …