61 citations · 152 across the 14 of their papers we have counts for
14 papers
Learn to Learn Metric Space for Few-Shot Segmentation of 3D Shapes
Xiang Li, Lingjing Wang, Yi Fang
Recent research has seen numerous supervised learning-based methods for 3D shape segmentation and remarkable performance has been achieved on various benchmark datasets. These supe…
3D Meta-Registration: Learning to Learn Registration of 3D Point Clouds
Lingjing Wang, Yu Hao, Xiang Li +1
Deep learning-based point cloud registration models are often generalized from extensive training over a large volume of data to learn the ability to predict the desired geometric…
3D Meta Point Signature: Learning to Learn 3D Point Signature for 3D Dense Shape Correspondence
Hao Huang, Lingjing Wang, Xiang Li +1
Point signature, a representation describing the structural neighborhood of a point in 3D shapes, can be applied to establish correspondences between points in 3D shapes. Conventio…
Deep-3DAligner: Unsupervised 3D Point Set Registration Network With Optimizable Latent Vector
Lingjing Wang, Xiang Li, Yi Fang
Point cloud registration is the process of aligning a pair of point sets via searching for a geometric transformation. Unlike classical optimization-based methods, recent learning-…
Unsupervised Partial Point Set Registration via Joint Shape Completion and Registration
Xiang Li, Lingjing Wang, Yi Fang
We propose a self-supervised method for partial point set registration. While recent proposed learning-based methods have achieved impressive registration performance on the full s…
Robust Image Matching By Dynamic Feature Selection
Hao Huang, Jianchun Chen, Xiang Li +2
Estimating dense correspondences between images is a long-standing image under-standing task. Recent works introduce convolutional neural networks (CNNs) to extract high-level feat…