144 citations · 454 across the 36 of their papers we have counts for
36 papers
Neural Gradient Learning and Optimization for Oriented Point Normal Estimation
Qing Li, Huifang Feng, Kanle Shi +3
We propose Neural Gradient Learning (NGL), a deep learning approach to learn gradient vectors with consistent orientation from 3D point clouds for normal estimation. It has excelle…
Retro-FPN: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation
Peng Xiang, Xin Wen, Yu-Shen Liu +3
Learning per-point semantic features from the hierarchical feature pyramid is essential for point cloud semantic segmentation. However, most previous methods suffered from ambiguou…
HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces
Qing Li, Yu-Shen Liu, Jin-San Cheng +3
We propose a novel normal estimation method called HSurf-Net, which can accurately predict normals from point clouds with noise and density variations. Previous methods focus on le…
CAP-UDF: Learning Unsigned Distance Functions Progressively from Raw Point Clouds with Consistency-Aware Field Optimization
Junsheng Zhou, Baorui Ma, Shujuan Li +3
Surface reconstruction for point clouds is an important task in 3D computer vision. Most of the latest methods resolve this problem by learning signed distance functions from point…
SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wild
Jie Qin, Shuaihang Yuan, Jiaxin Chen +22
Sketch-based 3D shape retrieval (SBSR) is an important yet challenging task, which has drawn more and more attention in recent years. Existing approaches address the problem in a r…
3D-OAE: Occlusion Auto-Encoders for Self-Supervised Learning on Point Clouds
Junsheng Zhou, Xin Wen, Baorui Ma +4
The manual annotation for large-scale point clouds is still tedious and unavailable for many harsh real-world tasks. Self-supervised learning, which is used on raw and unlabeled da…
3D Unsupervised Region-Aware Registration Transformer
Yu Hao, Yi Fang
This paper concerns the research problem of point cloud registration to find the rigid transformation to optimally align the source point set with the target one. Learning robust p…
3D Point Cloud Completion with Geometric-Aware Adversarial Augmentation
Mengxi Wu, Hao Huang, Yi Fang
With the popularity of 3D sensors in self-driving and other robotics applications, extensive research has focused on designing novel neural network architectures for accurate 3D po…
ContinuityLearner: Geometric Continuity Feature Learning for Lane Segmentation
Haoyu Fang, Jing Zhu, Yi Fang
Lane segmentation is a challenging issue in autonomous driving system designing because lane marks show weak textural consistency due to occlusion or extreme illumination but stron…
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…
G-VAE, a Geometric Convolutional VAE for ProteinStructure Generation
Hao Huang, Boulbaba Ben Amor, Xichan Lin +2
Analyzing the structure of proteins is a key part of understanding their functions and thus their role in biology at the molecular level. In addition, design new proteins in a meth…
Residual Networks as Flows of Velocity Fields for Diffeomorphic Time Series Alignment
Hao Huang, Boulbaba Ben Amor, Xichan Lin +2
Non-linear (large) time warping is a challenging source of nuisance in time-series analysis. In this paper, we propose a novel diffeomorphic temporal transformer network for both p…
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…