Large-scale pre-trained models have shown promising open-world performance for both
vision and language tasks. However, their transferred capacity on 3D point clouds is still …

Current 3D detection networks either rely on 2D object proposals or try to directly predict
bounding box parameters from each point in a scene. While former methods are dependent …

Recognition in open-world scenarios is an important and challenging field, where Vision-
Language Pre-training paradigms have greatly impacted the 2D domain. This inspires a …

Multi-view-based 3D object recognition is important in robot-environment interaction.
However, recent methods simply extract features from each view via convolutional neural …

Abstract 3D CT point clouds reconstructed from the original CT images are naturally
represented in real-world coordinates. Compared with CT images, 3D CT point clouds …

Sažetak Accurate detection and analysis of cracks is critical for ensuring the safety and
reliability of concrete bridges. Point cloud data (PCD) obtained from 3D scanning provides a …

3D object detection based on point clouds has important theoretical and application values.
As an innova-tive algorithm, VoteNet takes raw point clouds as input and uses a Hough vote …

Deep learning on 3D point clouds has drawn much attention, due to its large variety of
applications in intelligent perception for automated and robotic systems. Unlike structured …

Geometric intelligence is the aspect of human intelligence that relates to perceiving,
communicating and reasoning about geometries. Artificial agents like robots must possess …

Medical imaging has been applied widely in many clinical diagnoses to detect and
differentiate abnormalities by revealing the internal structure of the human body at normal …