3D segmentation is a fundamental and challenging problem in computer vision with
applications in autonomous driving and robotics. It has received significant attention from the …

Existing Transformer-based models for point cloud analysis suffer from quadratic complexity,
leading to compromised point cloud resolution and information loss. In contrast, the newly …

To advance the state of the art in the creation of 3D foundation models, this paper introduces
the ConDense framework for 3D pre-training utilizing existing pre-trained 2D networks and …

Transformers have revolutionized the point cloud learning task, but the quadratic complexity
hinders its extension to long sequence and makes a burden on limited computational …

Numerous prior studies predominantly emphasize constructing relation vectors for individual
neighborhood points and generating dynamic kernels for each vector and embedding these …

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 …

Automatic and accurate tooth segmentation on 3D dental point clouds plays a pivotal role in
computer-aided dentistry. Existing Transformer-based methods focus on aggregating local …

In recent years, remarkable ability has been demonstrated by the Transformer model in
capturing remote dependencies and improving point cloud segmentation performance …

Route planning for navigation under partial observability plays a crucial role in modern
robotics and autonomous driving. Existing route planning approaches can be categorized …

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