5. LiDAR-Camera Fusion 3D Object Detection
[Qi2018] Qi, C. R., Liu,W.,Wu, C., Su, H., & Guibas, L. J. (2018). Frustum
PointNets for 3D Object Detection from RGB-D Data. In Conference on
ComputerVision and Pattern Recognition.
[Ku2018]Ku, J., Mozifian, M., Lee, J., Harakeh,A., & Waslander, S. L. (2018).
Joint 3D Proposal Generation and Object Detection fromView
Aggregation. In International Conference on Intelligent Robots and Systems.
[Chen2017]Chen, X., Ma, H.,Wan, J., Li, B., & Xia,T. (2017). Multi-View 3D
Object Detection Network for Autonomous Driving. In Conference on
ComputerVision and Pattern Recognition.
[Liang2018]Liang, M.,Yang, B.,Wang, S., & Urtasun, R. (2018). Deep
Continuous Fusion for Multi-Sensor 3D Object Detection. In European
Conference on ComputerVision.
[Xu2018]Xu, D.,Anguelov, D., & Jain,A. (2018). PointFusion: Deep Sensor
Fusion for 3D Bounding Box Estimation. Conference on ComputerVision and
Pattern
[Du2018]Du, X., Jr, M. H.A., Karaman, S., Rus, D., & Feb, C.V. (2018).A
General Pipeline for 3D Detection ofVehicles. ArXiv, arXiv:1803.
23. LiDAR-Camera Fusion 2D Object Detection
[Premebida2014]Premebida, C., Carreira, J., Batista, J., & Nunes,
U. (2014). Pedestrian detection combining RGB and dense
LIDAR data. IEEE International Conference on Intelligent Robots
and Systems,
[Gonzalez2017]Gonzalez,A.,Vazquez, D., Lopez,A. M., &
Amores, J. (2017). On-Board Object Detection: Multicue,
Multimodal, and Multiview Random Forest of Local Experts.
IEEETransactions on Cybernetics, 47(11), 3980–3990.
[Costea2017]Costea,A. D.,Varga, R., & Nedevschi, S. (2017).
Fast Boosting based Detection using Scale Invariant Multimodal
Multiresolution Filtered Features. Conference on ComputerVision
and Pattern Recognition
[Asvadi2017]Asvadi,A., Garrote, L., Premebida, C., Peixoto, P., &
J. Nunes, U. (2017). Multimodal vehicle detection: Fusing 3D-
LIDAR and color camera data. Pattern Recognition Letters,
(September).
34. [付録]PointNet
34
Qi, C. R., Su, H., Mo, K., & Guibas, L. J. (2017). PointNet : Deep
Learning on Point Sets for 3D Classification and Segmentation
Big Data + Deep Representation Learning. IEEE Conference on
ComputerVision and Pattern Recognition (CVPR).
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