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It is laborious to manually label point cloud data for training high-quality 3D object detectors.
Pointpillars: Fast encoders for object detection from point clouds
Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: · 2007
Earlier work this paper cites.
Are we ready for autonomous driving? the KITTI vision benchmark suite
Geiger, A., Lenz, P., Urtasun, R.: · 2012
Earlier work this paper cites.
Are we ready for autonomous driving? the KITTI vision benchmark suite
Geiger, A., Lenz, P., Urtasun, R.: · 2012
Earlier work this paper cites.
3D object proposals for accurate object class detection
Chen, X., Kundu, K., Zhu, Y., Berneshawi, A.G., Ma, H., Fidler, S., Urtasun, R.: · 2015
Earlier work this paper cites.
Voxnet: A 3D convolutional neural network for real-time object recognition
Maturana, D., Scherer, S.: · 2015
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3D shapenets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: · 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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Multi-view convolutional neural networks for 3D shape recognition
Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.: · 2015
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A coarse-to-fine model for 3D pose estimation and sub-category recognition
Mottaghi, R., Xiang, Y., Savarese, S.: · 2015
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SUN RGB-D: A RGB-D scene understanding benchmark suite
Song, S., Lichtenberg, S.P., Xiao, J.: · 2015
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Monocular 3D object detection for autonomous driving
Chen, X., Kundu, K., Zhang, Z., Ma, H., Fidler, S., Urtasun, R.: · 2016
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Vehicle detection from 3D Lidar using fully convolutional network
Li, B., Zhang, T., Xia, T.: · 2016
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Volumetric and multi-view cnns for object classification on 3D data
Qi, C.R., Su, H., Niessner, M., Dai, A., Yan, M., Guibas, L.J.: · 2016
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What’s the point: Semantic segmentation with point supervision
Bearman, A., Russakovsky, O., Ferrari, V., Fei-Fei, L.: · 2016
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Semantic instance annotation of street scenes by 3D to 2D label transfer
Xie, J., Kiefel, M., Sun, M.T., Geiger, A.: · 2016
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Deep MANTA: A coarse-to-fine many-task network for joint 2D and 3D vehicle analysis from monocular image
Chabot, F., Chaouch, M., Rabarisoa, J., Teuliere, C., Chateau, T.: · 2017
Cited alongside, same era.
Multi-view 3D object detection network for autonomous driving
Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: · 2017
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.: · 2017
Cited alongside, same era.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: · 2017
Cited alongside, same era.
3D bounding box estimation using deep learning and geometry
Mousavian, A., Anguelov, D., Flynn, J., Kosecka, J.: · 2017
Cited alongside, same era.
Training object class detectors with click supervision
Fully-convolutional point networks for large-scale point clouds
Rethage, D., Wald, J., Sturm, J., Navab, N., Tombari, F.: · 2018
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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.: · 2018
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Deep continuous fusion for multi-sensor 3D object detection
Liang, M., Yang, B., Wang, S., Urtasun, R.: · 2018
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Deep extreme cut: From extreme points to object segmentation
Maninis, K.K., Caelles, S., Pont-Tuset, J., Van Gool, L.: · 2018
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Fast point R-CNN
Chen, Y., Liu, S., Shen, X., Jia, J.: · 2019
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Pointpillars: Fast encoders for object detection from point clouds
Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: · 2019
Later among the works it cites.
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Papadopoulos, D.P., Uijlings, J.R., Keller, F., Ferrari, V.: · 2017
Cited alongside, same era.
Extreme clicking for efficient object annotation
Papadopoulos, D.P., Uijlings, J.R., Keller, F., Ferrari, V.: · 2017
Cited alongside, same era.
Focal loss for dense object detection
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: · 2017
Cited alongside, same era.
Mask r-cnn
He, K., Gkioxari, G., Dollár, P., Girshick, R.: · 2017
Cited alongside, same era.
Pixor: Real-time 3D object detection from point clouds
Yang, B., Luo, W., Urtasun, R.: · 2018
Cited alongside, same era.
Leveraging pre-trained 3D object detection models for fast ground truth generation
Lee, J., Walsh, S., Harakeh, A., Waslander, S.L.: · 2018
Cited alongside, same era.
Second: Sparsely embedded convolutional detection
Yan, Y., Mao, Y., Li, B.: · 2018
Cited alongside, same era.
PointRCNN: 3D object proposal generation and detection from point cloud
Shi, S., Wang, X., Li, H.: · 2019
Later among the works it cites.
Autolabeling 3D objects with differentiable rendering of SDF shape priors
Zakharov, S., Kehl, W., Bhargava, A., Gaidon, A.: · 2019
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STD: Sparse-to-dense 3D object detector for point cloud
Yang, Z., Sun, Y., Liu, S., Shen, X., Jia, J.: · 2019
Later among the works it cites.
GS3D: An efficient 3D object detection framework for autonomous driving
Li, B., Ouyang, W., Sheng, L., Zeng, X., Wang, X.: · 2019
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Multi-task multi-sensor fusion for 3D object detection
Liang, M., Yang, B., Chen, Y., Hu, R., Urtasun, R.: · 2019
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Large-scale interactive object segmentation with human annotators
Benenson, R., Popov, S., Ferrari, V.: · 2019
Later among the works it cites.
The apolloscape open dataset for autonomous driving and its application
Wang, P., Huang, X., Cheng, X., Zhou, D., Geng, Q., Yang, R.: · 2019
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From points to parts: 3d object detection from point cloud with part-aware and part-aggregation network
Shi, S., Wang, Z., Shi, J., Wang, X., Li, H.: · 2020
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