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20172021
most citedLodoNet: A Deep Neural Network with 2D Keypoint Matchingfor 3D LiDAR Odometry Estimation

41 citations · 72 across the 10 of their papers we have counts for

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Showing 2020Show all

7 papers · 1 filter

cs.CV2020★ 41 cited

LodoNet: A Deep Neural Network with 2D Keypoint Matchingfor 3D LiDAR Odometry Estimation

Ce Zheng, Yecheng Lyu, Ming Li +1

Deep learning based LiDAR odometry (LO) estimation attracts increasing research interests in the field of autonomous driving and robotics. Existing works feed consecutive LiDAR fra…

cs.CV2020★ 1 cited

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…

eess.IV2020

RoadNet-RT: High Throughput CNN Architecture and SoC Design for Real-Time Road Segmentation

Lin Bai, Yecheng Lyu, Xinming Huang

In recent years, convolutional neural network has gained popularity in many engineering applications especially for computer vision. In order to achieve better performance, often m…

cs.CV2020★ 2 cited

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…

eess.SP2020★ 5 cited

A Unified Hardware Architecture for Convolutions and Deconvolutions in CNN

Lin Bai, Yecheng Lyu, Xinming Huang

In this paper, a scalable neural network hardware architecture for image segmentation is proposed. By sharing the same computing resources, both convolution and deconvolution opera…

eess.SP2020★ 1 cited

PointNet on FPGA for Real-Time LiDAR Point Cloud Processing

Lin Bai, Yecheng Lyu, Xin Xu +1

LiDAR sensors have been widely used in many autonomous vehicle modalities, such as perception, mapping, and localization. This paper presents an FPGA-based deep learning platform f…