Recently, point cloud domain adaptation (DA) practices have been implemented to improve
the generalization ability of deep learning models on point cloud data. However, variations …

Point cloud registration for evaluating the shape of 3D bent tubes is a preferred method for
improving the forming quality and reducing fabrication costs. In this process, large nonlinear …

Hyperspectral image (HSI) clustering has attracted increasing attention in recent years,
because it doesn't rely on labeled pixels. However, it is a challenging task due to the …

Region proposal-based detectors, such as Region-Convolutional Neural Networks (R-
CNNs), Fast R-CNNs, Faster R-CNNs, and Region-Based Fully Convolutional Networks (R …

Point cloud registration is pivotal across various applications, yet traditional methods rely on
unordered point clouds, leading to significant challenges in terms of computational …

Anomaly detection for hyperspectral images (HSIs) is a challenging problem to distinguish a
few anomalous pixels from a majority of background pixels. Most existing methods cannot …

3D keypoint detection endeavors to identify well-aligned and semantically consistent
elements that reflect object shapes within point clouds, which plays a significant role in wide …

Manual annotation of large-scale point cloud datasets is laborious due to their irregular
structure. While cross-modal contrastive learning methods such as CrossPoint and CrossNet …

The automobile hub plays a crucial role in supporting the weight of the entire vehicle and
transmitting power, and the measurement accuracy of critical dimensions is closely related …

Point cloud registration is a challenging task when only partially visible data is available.
Recently, many learning-based methods have been proposed for this problem and have …