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Lidar based 3D object detection is inevitable for autonomous driving, because it directly links to environmental understanding and therefore builds the base for prediction and motion planning.
Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A.: · 2012
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: · 2013
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3d shapenets for 2.5d object recognition and next-best-view prediction
Wu, Z., Song, S., Khosla, A., Tang, X., Xiao, J.: · 2014
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Voting for voting in online point cloud object detection
Wang, D.Z., Posner, I.: · 2015
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You only look once: Unified, real-time object detection
Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: · 2015
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SSD: single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C., Berg, A.C.: · 2015
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Faster R-CNN: towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R.B., Sun, J.: · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
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Girshick, R.B.: · 2015
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Data-driven 3d voxel patterns for object category recognition
Xiang, Y., Choi, W., Lin, Y., Savarese, S.: · 2015
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Biternion nets: Continuous head pose regression from discrete training labels
Beyer, L., Hermans, A., Leibe, B.: · 2015
Cited alongside, same era.
3d object proposals for accurate object class detection
Chen, X., Kundu, K., Zhu, Y., Berneshawi, A., Ma, H., Fidler, S., Urtasun, R.: · 2015
Cited alongside, same era.
Multi-view 3d object detection network for autonomous driving
Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: · 2016
Cited alongside, same era.
Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks
Engelcke, M., Rao, D., Wang, D.Z., Tong, C.H., Posner, I.: · 2016
Cited alongside, same era.
Vehicle detection from 3d lidar using fully convolutional network
Li, B., Zhang, T., Xia, T.: · 2016
Cited alongside, same era.
3d object proposals using stereo imagery for accurate object class detection
Chen, X., Kundu, K., Zhu, Y., Ma, H., Fidler, S., Urtasun, R.: · 2016
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Darknet: Open source neural networks in c
Redmon, J.: · 2016
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Voxelnet: End-to-end learning for point cloud based 3d object detection
Zhou, Y., Tuzel, O.: · 2017
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Frustum pointnets for 3d object detection from RGB-D data
Qi, C.R., Liu, W., Wu, C., Su, H., Guibas, L.J.: · 2017
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Joint 3d proposal generation and object detection from view aggregation
Ku, J., Mozifian, M., Lee, J., Harakeh, A., Waslander, S.: · 2017
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3d fully convolutional network for vehicle detection in point cloud
Li, B.: · 2016
Cited alongside, same era.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: · 2016
Cited alongside, same era.
YOLO9000: better, faster, stronger
Redmon, J., Farhadi, A.: · 2016
Cited alongside, same era.
A unified multi-scale deep convolutional neural network for fast object detection
Cai, Z., Fan, Q., Feris, R.S., Vasconcelos, N.: · 2016
Cited alongside, same era.
Monocular 3d object detection for autonomous driving
Chen, X., Kundu, K., Zhang, Z., Ma, H., Fidler, S., Urtasun, R.: · 2016
Cited alongside, same era.
Later among the works it cites.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: · 2017
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Accurate single stage detector using recurrent rolling convolution
Ren, J.S.J., Chen, X., Liu, J., Sun, W., Pang, J., Yan, Q., Tai, Y., Xu, L.: · 2017
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Pointcnn (2018)
Li, Y., Bu, R., Sun, M., Chen, B.: · 2018
Closest in time.
Dynamic graph cnn for learning on point clouds (2018)
Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: · 2018
Closest in time.