RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving

Voxelnet: End-to-end learning for point cloud based 3d object detection

Y. Zhou and O. Tuzel · 1906

Earlier work this paper cites.

Lidar-based 3d object perception

M. Himmelsbach, A. Mueller, T. Lüttel, and H.-J. Wünsche · 2008

Earlier work this paper cites.

ImageNet: A large-scale hierarchical image database

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009

Earlier work this paper cites.

Deep convolutional networks for scene parsing

D. Grangier, L. Bottou, and R. Collobert · 2009

Earlier work this paper cites.

On the segmentation of 3d lidar point clouds

B. Douillard, J. Underwood, N. Kuntz, V. Vlaskine, A. Quadros, P. Morton, and A. Frenkel · 2011

Earlier work this paper cites.

Learning hierarchical features for scene labeling

C. Farabet, C. Couprie, L. Najman, and Y. LeCun · 2013

Earlier work this paper cites.

Vision meets robotics: The kitti dataset

A. Geiger, P. Lenz, C. Stiller, and R. Urtasun · 2013

Earlier work this paper cites.

Vision-based driver assistance systems: Survey, taxonomy and advances

J. Horgan, C. Hughes, J. McDonald, and S. Yogamani · 2015

Earlier work this paper cites.

Fully convolutional networks for semantic segmentation

J. Long, E. Shelhamer, and T. Darrell · 2015

Cited alongside, same era.

Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size

Original

F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer · 2016

Cited alongside, same era.

Segnet: A deep convolutional encoder-decoder architecture for image segmentation

V. Badrinarayanan, A. Kendall, and R. Cipolla · 2017

Cited alongside, same era.

Deep convolution long-short term memory network for lidar semantic segmentation

K. Elmadawy, A. Al Sallab, M. Gamal, M. Abdelrazek, H. Eraqi, J. Honer, A. Valeo, and E. C. Cairo · 2017

Cited alongside, same era.

Computer vision in automated parking systems: Design, implementation and challenges

M. Heimberger, J. Horgan, C. Hughes, J. McDonald, and S. Yogamani · 2017

Cited alongside, same era.

Analysis of efficient cnn design techniques for semantic segmentation

A. Briot, P. Viswanath, and S. Yogamani · 2018

Later among the works it cites.

Monocular fisheye camera depth estimation using sparse lidar supervision

V. R. Kumar, S. Milz, C. Witt, M. Simon, K. Amende, J. Petzold, S. Yogamani, and T. Pech · 2018

Later among the works it cites.

Real-time dynamic object detection for autonomous driving using prior 3d-maps

B. Ravi Kiran, L. Roldao, B. Irastorza, R. Verastegui, S. Suss, S. Yogamani, V. Talpaert, et al · 2018

Later among the works it cites.

Rtseg: Real-time semantic segmentation comparative study

M. Siam, M. Gamal, M. Abdel-Razek, S. Yogamani, and M. Jagersand · 2018

Later among the works it cites.

Pointseg: Real-time semantic segmentation based on 3d lidar point cloud

Original

Y. Wang, T. Shi, P. Yun, L. Tai, and M. Liu · 2018

Later among the works it cites.