5 citations · 9 across the 8 of their papers we have counts for
8 papers
Synthetic Data Generation and Vision-based Wrinkle and Keypoint Detection for Bimanual Cloth Manipulation
Ariel Herrera, Xueyang Kang, Atal Anil Kumar
Robotic manipulation of textiles remains challenging because continuous deformation and self-occlusions hinder the robust visual perception required to estimate the cloth's state.…
ClickSeg3D: Few-Click Interactive Segmentation via Semantic Embeddings
Xueyang Kang, Zijian Yu, Kourosh Khoshelham +1
Interactive segmentation allows efficient label generation by leveraging user-provided clicks to progressively refine predictions, which is critical when fully supervised labels ar…
Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detection
Xueyang Kang, Zizhao Li, Tian Lan +3
3D shape anomaly detection is a crucial task for industrial inspection and geometric analysis. Existing deep learning approaches typically learn representations of normal shapes an…
Look Beyond: Two-Stage Scene View Generation via Panorama and Video Diffusion
Xueyang Kang, Zhengkang Xiang, Zezheng Zhang +1
Novel view synthesis (NVS) from a single image is highly ill-posed due to large unobserved regions, especially for views that deviate significantly from the input. While existing m…
Surfel-based 3D Registration with Equivariant SE(3) Features
Xueyang Kang, Hang Zhao, Kourosh Khoshelham +1
Point cloud registration is crucial for ensuring 3D alignment consistency of multiple local point clouds in 3D reconstruction for remote sensing or digital heritage. While various…
Out-of-distribution detection in 3D applications: a review
Zizhao Li, Xueyang Kang, Joseph West +1
The ability to detect objects that are not prevalent in the training set is a critical capability in many 3D applications, including autonomous driving. Machine learning methods fo…
Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registration
Xueyang Kang, Zhaoliang Luan, Kourosh Khoshelham +1
Point cloud registration is a foundational task for 3D alignment and reconstruction applications. While both traditional and learning-based registration approaches have succeeded,…
FocDepthFormer: Transformer with latent LSTM for Depth Estimation from Focal Stack
Xueyang Kang, Fengze Han, Abdur R. Fayjie +3
Most existing methods for depth estimation from a focal stack of images employ convolutional neural networks (CNNs) using 2D or 3D convolutions over a fixed set of images. However,…