144 citations · 454 across the 36 of their papers we have counts for
14 papers · 1 filter
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…
GP-Aligner: Unsupervised Non-rigid Groupwise Point Set Registration Based On Optimized Group Latent Descriptor
Lingjing Wang, Xiang Li, Yi Fang
In this paper, we propose a novel method named GP-Aligner to deal with the problem of non-rigid groupwise point set registration. Compared to previous non-learning approaches, our…
Meta Deformation Network: Meta Functionals for Shape Correspondence
Daohan Lu, Yi Fang
We present a new technique named "Meta Deformation Network" for 3D shape matching via deformation, in which a deep neural network maps a reference shape onto the parameters of a se…
3DMotion-Net: Learning Continuous Flow Function for 3D Motion Prediction
Shuaihang Yuan, Xiang Li, Anthony Tzes +1
In this paper, we deal with the problem to predict the future 3D motions of 3D object scans from previous two consecutive frames. Previous methods mostly focus on sparse motion pre…
DeepTracking-Net: 3D Tracking with Unsupervised Learning of Continuous Flow
Shuaihang Yuan, Xiang Li, Yi Fang
This paper deals with the problem of 3D tracking, i.e., to find dense correspondences in a sequence of time-varying 3D shapes. Despite deep learning approaches have achieved promis…
Unsupervised Learning of Global Registration of Temporal Sequence of Point Clouds
Lingjing Wang, Yi Shi, Xiang Li +1
Global registration of point clouds aims to find an optimal alignment of a sequence of 2D or 3D point sets. In this paper, we present a novel method that takes advantage of current…
Few-shot Object Detection on Remote Sensing Images
Jingyu Deng, Xiang Li, Yi Fang
In this paper, we deal with the problem of object detection on remote sensing images. Previous methods have developed numerous deep CNN-based methods for object detection on remote…
Unsupervised Learning of 3D Point Set Registration
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. Recent works leverage the power of deep learning for register…
Height estimation from single aerial images using a deep ordinal regression network
Xiang Li, Mingyang Wang, Yi Fang
Understanding the 3D geometric structure of the Earth's surface has been an active research topic in photogrammetry and remote sensing community for decades, serving as an essentia…
Geometry-Aware Segmentation of Remote Sensing Images via Implicit Height Estimation
Xiang Li, Lingjing Wang, Yi Fang
Recent studies have shown the benefits of using additional elevation data (e.g., DSM) for enhancing the performance of the semantic segmentation of aerial images. However, previous…