82 citations · 206 across the 32 of their papers we have counts for
22 papers · 1 filter
PRISE: Demystifying Deep Lucas-Kanade with Strongly Star-Convex Constraints for Multimodel Image Alignment
Yiqing Zhang, Xinming Huang, Ziming Zhang
The Lucas-Kanade (LK) method is a classic iterative homography estimation algorithm for image alignment, but often suffers from poor local optimality especially when image pairs ha…
Self-supervised Geometric Features Discovery via Interpretable Attentio for Vehicle Re-Identification and Beyond (Complete Version)
Ming Li, Xinming Huang, Ziming Zhang
To learn distinguishable patterns, most of recent works in vehicle re-identification (ReID) struggled to redevelop official benchmarks to provide various supervisions, which requir…
A Survey on Visual Map Localization Using LiDARs and Cameras
Elhousni Mahdi, Huang Xinming
As the autonomous driving industry is slowly maturing, visual map localization is quickly becoming the standard approach to localize cars as accurately as possible. Owing to the ri…
A Near Sensor Edge Computing System for Point Cloud Semantic Segmentation
Lin Bai, Yiming Zhao, Xinming Huang
Point cloud semantic segmentation has attracted attentions due to its robustness to light condition. This makes it an ideal semantic solution for autonomous driving. However, consi…
CoFi: Coarse-to-Fine ICP for LiDAR Localization in an Efficient Long-lasting Point Cloud Map
Yecheng Lyu, Xinming Huang, Ziming Zhang
LiDAR odometry and localization has attracted increasing research interest in recent years. In the existing works, iterative closest point (ICP) is widely used since it is precise…
A Divide-and-Merge Point Cloud Clustering Algorithm for LiDAR Panoptic Segmentation
Yiming Zhao, Xiao Zhang, Xinming Huang
Clustering objects from the LiDAR point cloud is an important research problem with many applications such as autonomous driving. To meet the real-time requirement, existing resear…
FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decoding
Yiming Zhao, Lin Bai, Xinming Huang
Projecting the point cloud on the 2D spherical range image transforms the LiDAR semantic segmentation to a 2D segmentation task on the range image. However, the LiDAR range image i…
A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation
Yiming Zhao, Xiao Zhang, Xinming Huang
LiDAR panoptic segmentation is a newly proposed technical task for autonomous driving. In contrast to popular end-to-end deep learning solutions, we propose a hybrid method with an…
Revisiting 2D Convolutional Neural Networks for Graph-based Applications
Yecheng Lyu, Xinming Huang, Ziming Zhang
Graph convolutional networks (GCNs) are widely used in graph-based applications such as graph classification and segmentation. However, current GCNs have limitations on implementat…
Enabling 3D Object Detection with a Low-Resolution LiDAR
Lin Bai, Yiming Zhao, Xinming Huang
Light Detection And Ranging (LiDAR) has been widely used in autonomous vehicles for perception and localization. However, the cost of a high-resolution LiDAR is still prohibitively…
Deep Lucas-Kanade Homography for Multimodal Image Alignment
Yiming Zhao, Xinming Huang, Ziming Zhang
Estimating homography to align image pairs captured by different sensors or image pairs with large appearance changes is an important and general challenge for many computer vision…
A Surface Geometry Model for LiDAR Depth Completion
Yiming Zhao, Lin Bai, Ziming Zhang +1
LiDAR depth completion is a task that predicts depth values for every pixel on the corresponding camera frame, although only sparse LiDAR points are available. Most of the existing…
EllipsoidNet: Ellipsoid Representation for Point Cloud Classification and Segmentation
Yecheng Lyu, Xinming Huang, Ziming Zhang
Point cloud patterns are hard to learn because of the implicit local geometry features among the orderless points. In recent years, point cloud representation in 2D space has attra…
Aerial Height Prediction and Refinement Neural Networks with Semantic and Geometric Guidance
Elhousni Mahdi, Zhang Ziming, Huang Xinming
Deep learning provides a powerful new approach to many computer vision tasks. Height prediction from aerial images is one of those tasks that benefited greatly from the deployment…
Self-supervised Geometric Features Discovery via Interpretable Attention for Vehicle Re-Identification and Beyond
Ming Li, Xinming Huang, Ziming Zhang
To learn distinguishable patterns, most of recent works in vehicle re-identification (ReID) struggled to redevelop official benchmarks to provide various supervisions, which requir…
TreeRNN: Topology-Preserving Deep GraphEmbedding and Learning
Yecheng Lyu, Ming Li, Xinming Huang +3
General graphs are difficult for learning due to their irregular structures. Existing works employ message passing along graph edges to extract local patterns using customized grap…
A Survey on 3D LiDAR Localization for Autonomous Vehicles
Mahdi Elhousni, Xinming Huang
LiDAR sensors are becoming one of the most essential sensors in achieving full autonomy for self driving cars. LiDARs are able to produce rich, dense and precise spatial data, whic…
Automatic Building and Labeling of HD Maps with Deep Learning
Mahdi Elhousni, Yecheng Lyu, Ziming Zhang +1
In a world where autonomous driving cars are becoming increasingly more common, creating an adequate infrastructure for this new technology is essential. This includes building and…
Pedestrian Tracking with Gated Recurrent Units and Attention Mechanisms
Mahdi Elhousni, Xinming Huang
Pedestrian tracking has long been considered an important problem, especially in security applications. Previously,many approaches have been proposed with various types of sensors.…
Learning to Segment 3D Point Clouds in 2D Image Space
Yecheng Lyu, Xinming Huang, Ziming Zhang
In contrast to the literature where local patterns in 3D point clouds are captured by customized convolutional operators, in this paper we study the problem of how to effectively a…
Road Segmentation Using CNN and Distributed LSTM
Yecheng Lyu, Lin Bai, Xinming Huang
In automated driving systems (ADS) and advanced driver-assistance systems (ADAS), an efficient road segmentation is necessary to perceive the drivable region and build an occupancy…
Road Segmentation Using CNN with GRU
Yecheng Lyu, Xinming Huang
This paper presents an accurate and fast algorithm for road segmentation using convolutional neural network (CNN) and gated recurrent units (GRU). For autonomous vehicles, road seg…