Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

21 February 2019

Papers citing "Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges"

8 / 108 papers shown

Title
A Survey of End-to-End Driving: Architectures and Training Methods Ardi Tampuu Maksym Semikin Naveed Muhammad D. Fishman Tambet Matiisen 3DV 23 228 0 13 Mar 2020
AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance Ilker Bozcan Erdal Kayacan 49 136 0 31 Jan 2020
CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description Deyu Yin Qian Zhang Jingbin Liu Xinlian Liang Yunshen Wang Jyri Maanpää Hao Ma Juha Hyyppa Ruizhi Chen 3DPC 33 21 0 06 Jan 2020
epBRM: Improving a Quality of 3D Object Detection using End Point Box Regression Module K. Shin M. Tomizuka 3DPC 30 4 0 27 Sep 2019
A Survey of Autonomous Driving: Common Practices and Emerging Technologies Ekim Yurtsever Jacob Lambert Alexander Carballo K. Takeda 37 1,344 0 12 Jun 2019
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 271 5,329 0 05 Nov 2016
Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks Martin Engelcke Dushyant Rao Dominic Zeng Wang Chi Hay Tong Ingmar Posner 3DPC 192 522 0 21 Sep 2016
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation Adam Paszke Abhishek Chaurasia Sangpil Kim Eugenio Culurciello SSeg 233 2,056 0 07 Jun 2016