Vehicle Detection from 3D Lidar Using Fully Convolutional Network

You Only Look Once: Unified, Real-Time Object Detection

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · see citing works

Are we ready for autonomous driving? The KITTI vision benchmark suite

Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897 · see citing works

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works

3D ShapeNets: A deep representation for volumetric shapes

Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., & Xiao, J. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298801 · Cited by 5,413 · see citing works

Caffe: Convolutional Architecture for Fast Feature EmbeddingPreprint

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 4,748 · see citing works

VoxNet: A 3D Convolutional Neural Network for real-time object recognition

Maturana, D., & Scherer, S. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353481 · Cited by 3,674 · see citing works

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6229 · Cited by 2,680 · see citing works

Using spin images for efficient object recognition in cluttered 3D scenes

Johnson, A., & Hebert, M. (1999) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.765655 · Cited by 2,656 · see citing works

Fully Convolutional Networks for Semantic SegmentationPreprint

Long, J., Shelhamer, E., & Darrell, T. J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.4038 · Cited by 2,490 · see citing works

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

Gupta, S., Girshick, R., Arbeláez, P., & Malik, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10584-0_23 · Cited by 1,528 · see citing works

The Representation, Recognition, and Locating of 3-D Objects

Faugeras, O. D., & Hebert, M. (1986) · The International Journal of Robotics Research · doi:10.1177/027836498600500302 · Cited by 800 · see citing works

Untitled work

(2015) · Cited by 656 · see citing works

Robust vehicle localization in urban environments using probabilistic maps

Levinson, J., & Thrun, S. (2010) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2010.5509700 · Cited by 585 · see citing works

Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds

Papon, J. A., Abramov, A., Schoeler, M., & Wörgötter, F. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.264 · Cited by 561 · see citing works

Convolutional-Recursive Deep Learning for 3D Object Classification

Socher, R., Huval, B., Bath, B., Manning, C. D., & Ng, A. Y. (2012) · Cited by 507 · see citing works

On the segmentation of 3D LIDAR point clouds

Douillard, B., Underwood, J., Kuntz, N. R., Vlaskine, V., Quadros, A., Morton, P., & Frenkel, A. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5979818 · Cited by 477 · see citing works

Fast segmentation of 3D point clouds for ground vehicles

Himmelsbach, M., Hundelshausen, F. V., & Wuensche, H. (2010) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2010.5548059 · Cited by 450 · see citing works

DenseBox: Unifying Landmark Localization with End to End Object Detection

Huang, L., Yang, Y., Deng, Y., & Yu, Y. (2015) · arXiv · doi:10.48550/arxiv.1509.04874 · Cited by 407 · see citing works

Sliding Shapes for 3D Object Detection in Depth Images

Song, S., & Xiao, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10599-4_41 · Cited by 361 · see citing works

Voting for Voting in Online Point Cloud Object Detection

Zeng Wang, D., & Posner, I. (2015) · Robotics: Science and Systems XI · doi:10.15607/rss.2015.xi.035 · Cited by 337 · see citing works

Unsupervised feature learning for 3D scene labeling

Lai, K., Bo, L., & Fox, D. (2014) · 2014 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2014.6907298 · Cited by 317 · see citing works

RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features

Schwarz, M., Schulz, H., & Behnke, S. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139363 · Cited by 316 · see citing works

Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion

Moosmann, F., Pink, O., & Stiller, C. (2009) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2009.5164280 · Cited by 302 · see citing works

Holistic Scene Understanding for 3D Object Detection with RGBD Cameras

Lin, D., Fidler, S., & Urtasun, R. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.179 · Cited by 253 · see citing works

A clustering method for efficient segmentation of 3D laser data

Klasing, K., Wollherr, D., & BUSS, M. J. (2008) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2008.4543832 · Cited by 179 · see citing works

Towards 3D object recognition via classification of arbitrary object tracks

Teichman, A., Levinson, J., & Thrun, S. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5979636 · Cited by 173 · see citing works

What could move? Finding cars, pedestrians and bicyclists in 3D laser data

Wang, D. Z., Posner, I., & Newman, P. M. (2012) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2012.6224734 · Cited by 103 · see citing works

Performance of histogram descriptors for the classification of 3D laser range data in urban environments

Behley, J., Steinhage, V., & Cremers, A. B. (2012) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2012.6225003 · Cited by 99 · see citing works

Laser-based segment classification using a mixture of bag-of-words

Behley, J., Steinhage, V., & Cremers, A. B. (2013) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2013.6696957 · Cited by 98 · see citing works

Instance-based AMN classification for improved object recognition in 2D and 3D laser range data

Triebel, R., Schmidt, R. K., Mozos, Ó. M., & Burgard, W. (2007) · Lincoln Repository (University of Lincoln) · Cited by 54 · see citing works

Segmentation and Unsupervised Part-based Discovery of Repetitive Objects

Triebel, R., Shin, J., & Siegwart, R. (2010) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-010027929 · Cited by 52 · see citing works

Segmentation and Unsupervised Part-based Discovery of Repetitive Objects

Triebel, R., Shin, J., & Siegwart, R. (2010) · doi:10.15607/rss.2010.vi.009 · Cited by 33 · see citing works

Learning Rich Features from RGB-D Images for Object Detection and SegmentationPreprint

Gupta, S., Girshick, R., Arbeláez, P., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.5736 · Cited by 4 · see citing works

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

Zhou, Y., & Tuzel, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00472 · Cited by 4,813 · see citing works

PointPillars: Fast Encoders for Object Detection From Point Clouds

Lang, A. H., Vora, S., Caesar, H., Zhou, L., Yang, J., & Beijbom, O. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01298 · Cited by 4,673 · see citing works

Multi-view 3D Object Detection Network for Autonomous Driving

Chen, X., Ma, H., Wan, J., Li, B., & Xia, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.691 · Cited by 3,450 · see citing works

Point Transformer

Zhao, H., Jiang, L., Jia, J., Torr, P. H. S., & Koltun, V. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01595 · Cited by 2,847 · see citing works

Frustum PointNets for 3D Object Detection from RGB-D Data

Qi, C. R., Liu, W., Wu, C., Su, H., & Guibas, L. J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00102 · Cited by 2,655 · see citing works

Deep Learning for 3D Point Clouds: A Survey

Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L., & Bennamoun, M. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3005434 · Cited by 2,445 · see citing works

RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., & Markham, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01112 · Cited by 2,275 · see citing works

A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i>

Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889 · see citing works

Joint 3D Proposal Generation and Object Detection from View Aggregation

Ku, J., Mozifian, M., Lee, J., Harakeh, A., & Waslander, S. L. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594049 · Cited by 1,697 · see citing works

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

Feng, D., Haase-Schutz, C., Rosenbaum, L., Hertlein, H., Glaser, C., Timm, F., Wiesbeck, W., & Dietmayer, K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2972974 · Cited by 1,425 · see citing works

PIXOR: Real-time 3D Object Detection from Point Clouds

Yang, B., Luo, W., & Urtasun, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00798 · Cited by 1,249 · see citing works

PointPainting: Sequential Fusion for 3D Object Detection

Vora, S., Lang, A. H., Helou, B., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00466 · Cited by 1,236 · see citing works

Deep Continuous Fusion for Multi-sensor 3D Object Detection

Liang, M., Yang, B., Wang, S., & Urtasun, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01270-0_39 · Cited by 993 · see citing works

SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

Wu, B., Wan, A., Yue, X., & Keutzer, K. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8462926 · Cited by 967 · see citing works

Sparsity Invariant CNNs

Uhrig, J., Schneider, N., Schneider, L., Franke, U., Brox, T., & Geiger, A. (2017) · 2017 International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00012 · Cited by 924 · see citing works

PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation

Xu, D., Anguelov, D., & Jain, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00033 · Cited by 856 · see citing works

A Survey on 3D Object Detection Methods for Autonomous Driving Applications

Arnold, E. H., Al-Jarrah, O. Y., Dianati, M., Fallah, S., Oxtoby, D., & Mouzakitis, A. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2892405 · Cited by 720 · see citing works

Point Transformer V3: Simpler, Faster, Stronger

Wu, X., Jiang, L., Wang, P., Liu, Z., Liu, X., Qiao, Y., Ouyang, W., He, T., & Zhao, H. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00463 · Cited by 727 · see citing works

Computer Vision for Autonomous Vehicles

Janai, J., Güney, F., Behl, A., & Geiger, A. (2020) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000079 · Cited by 664 · see citing works

Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review

Li, Y., Ma, L., Zhong, Z., Liu, F., Chapman, M. A., Cao, D., & Li, J. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015992 · Cited by 619 · see citing works

Stereo R-CNN Based 3D Object Detection for Autonomous Driving

Li, P., Chen, X., & Shen, S. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00783 · Cited by 597 · see citing works

Mvx-net: Multimodal voxelnet for 3d object detection

Sindagi, V. A., Zhou, Y., & Tuzel, O. (2019) · 2019 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2019.8794195 · Cited by 554 · see citing works

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. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989161 · Cited by 549 · see citing works

Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection

Wang, Z., & Jia, K. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968513 · Cited by 547 · see citing works

3D fully convolutional network for vehicle detection in point cloud

Li, B. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8205955 · Cited by 488 · see citing works

3D Object Detection for Autonomous Driving: A Comprehensive Survey

Mao, J., Shi, S., Wang, X., & Li, H. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01790-1 · Cited by 492 · see citing works

3D Object Detection for Autonomous Driving: A Survey

Qian, R., Lai, X., & Li, X. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108796 · Cited by 471 · see citing works

CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection

Nabati, R., & Qi, H. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00157 · Cited by 461 · see citing works

SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation

Liu, Z., Wu, Z., & Tóth, R. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00506 · Cited by 457 · see citing works

3D Object Proposals Using Stereo Imagery for Accurate Object Class Detection

Chen, X., Kundu, K., Zhu, Y., Ma, H., Fidler, S., & Urtasun, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2706685 · Cited by 424 · see citing works

Fast Point R-CNN

Chen, Y., Liu, S., Shen, X., & Jia, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00987 · Cited by 406 · see citing works

TANet: Robust 3D Object Detection from Point Clouds with Triple Attention

Liu, Z., Zhao, X., Huang, T., Hu, R., Zhou, Y., & Bai, X. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6837 · Cited by 395 · see citing works

LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving

Meyer, G. P., Laddha, A., Kee, E., Vallespi-Gonzalez, C., & Wellington, C. K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01296 · Cited by 374 · see citing works

Multi-level Fusion Based 3D Object Detection from Monocular Images

Xu, B., & Chen, Z. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00249 · Cited by 358 · see citing works

Complex-YOLO: An Euler-Region-Proposal for Real-Time 3D Object Detection on Point Clouds

Simón, M., Milz, S., Amende, K., & Groß, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11009-3_11 · Cited by 360 · see citing works

Cooperative Perception for 3D Object Detection in Driving Scenarios Using Infrastructure Sensors

Arnold, E. H., Dianati, M., de Temple, R., & Fallah, S. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3028424 · Cited by 335 · see citing works

FUTR3D: A Unified Sensor Fusion Framework for 3D Detection

Chen, X., Zhang, T., Wang, Y., Wang, Y., & Zhao, H. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00022 · Cited by 337 · see citing works

Fusion of 3D LIDAR and Camera Data for Object Detection in Autonomous Vehicle Applications

Zhao, X., Sun, P., Xu, Z., Min, H., & Yu, H. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2966034 · Cited by 320 · see citing works

A Review of Sensor Technologies for Perception in Automated Driving

Martí, E., de Miguel, M. Á., García, F., & Perez, J. (2019) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2019.2907630 · Cited by 315 · see citing works

Enhancing transportation systems via deep learning: A survey

Yuan, W., Zhang, D., Liu, Y., Dai, B., & Lee, L. H. (2018) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2018.12.004 · Cited by 315 · see citing works

Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds

Simon, M., Amende, K., Kraus, A., Honer, J., Samann, T., Kaulbersch, H., Milz, S., & Gross, H. M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00158 · Cited by 304 · see citing works

Path planning algorithms in the autonomous driving system: A comprehensive review

Reda, M., Onsy, A., Ghanbari, A., & Haikal, A. (2024) · Robotics Auton. Syst. · doi:10.1016/j.robot.2024.104630 · Cited by 312 · see citing works

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

Thombre, S., Zhao, Z. M., Ramm-Schmidt, H., García, J. M. V., Malkamäki, T., Nikolskiy, S., Hammarberg, T., Nuortie, H., Bhuiyan, M. Z. H., Särkkä, S., et al. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3023957 · Cited by 296 · see citing works

Improving 3D Object Detection with Channel-wise Transformer

Shenga, H., Cai, S., Liu, Y., Deng, B., Huang, J., Hua, X.-S., & Zhao, M.-J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00274 · Cited by 296 · see citing works

Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous DrivingPreprint

You, Y., Wang, Y., Chao, W., Garg, D., Pleiss, G., Hariharan, B., Campbell, M., & Weinberger, K. Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.06310 · Cited by 316 · see citing works

Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review

Yao, S., Guan, R., Huang, X., Li, Z., Sha, X., Yue, Y., Lim, E. G., Seo, H., Man, K. L., Zhu, X., et al. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3307157 · Cited by 287 · see citing works

Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy

Fernandes, D., Silva, A., Névoa, R., Simões, C., Gonzalez, D. G., Guevara, M., Novais, P., Monteiro, J., & Melo-Pinto, P. (2021) · Information Fusion · doi:10.1016/j.inffus.2020.11.002 · Cited by 240 · see citing works

Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection

Feng, D., Rosenbaum, L., & Dietmayer, K. (2018) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8569814 · Cited by 260 · see citing works

RangeDet: In Defense of Range View for LiDAR-based 3D Object Detection

Fan, L., Xiong, X., Feng, W., Wang, N., & Zhang, Z. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00291 · Cited by 260 · see citing works

A Review of Vehicle Detection Techniques for Intelligent Vehicles

Wang, Z., Zhan, J., Duan, C., Guan, X., Lu, P., & Yang, K. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3128968 · Cited by 258 · see citing works

LiDAR R-CNN: An Efficient and Universal 3D Object Detector

Li, Z., Wang, F., & Wang, N. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00746 · Cited by 246 · see citing works

Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art

Janai, J., Güney, F., Behl, A., & Geiger, A. (2020) · doi:10.1561/9781680836899 · Cited by 243 · see citing works

Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds

Yang, B., Wang, J., Clark, R., Hu, Q., Wang, S., Markham, A., & Trigoni, N. (2019) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.48550/arxiv.1906.01140 · Cited by 243 · see citing works

Joint Activity Recognition and Indoor Localization With WiFi Fingerprints

Wang, F., Feng, J., Zhao, Y., Zhang, X., Zhang, S., & Han, J. (2019) · IEEE Access · doi:10.1109/access.2019.2923743 · Cited by 228 · see citing works

Multimodal vehicle detection: fusing 3D-LIDAR and color camera data

Asvadi, A., Garrote, L., Premebida, C., Peixoto, P., & J. Nunes, U. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.09.038 · Cited by 225 · see citing works

Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling

Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., & Markham, A. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3083288 · Cited by 228 · see citing works

SegMap: Segment-based mapping and localization using data-driven descriptors

Dubé, R., Cramariuc, A., Dugas, D., Sommer, H., Dymczyk, M., Nieto, J. I., Siegwart, R., & Cadena, C. (2019) · The International Journal of Robotics Research · doi:10.1177/0278364919863090 · Cited by 220 · see citing works

RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement

Shin, K., Kwon, Y. P., & Tomizuka, M. (2019) · 2019 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2019.8813895 · Cited by 221 · see citing works

End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point CloudsPreprint

Zhou, Y., Sun, P., Zhang, Y., Anguelov, D., Gao, J., Ouyang, T. Y., Guo, J. C. Y., Ngiam, J., & Vasudevan, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.06528 · Cited by 237 · see citing works

Deep 3D Object Detection Networks Using LiDAR Data: A Review

Wu, Y., Wang, Y., Zhang, S., & Ogai, H. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3020626 · Cited by 217 · see citing works

Orthographic feature transform for monocular 3d object detection

Roddick, T., Kendall, A., & Cipolla, R. (2018) · arXiv · doi:10.48550/arxiv.1811.08188 · Cited by 211 · see citing works

End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection

Qian, R., Garg, D., Wang, Y., You, Y., Belongie, S., Hariharan, B., Campbell, M., Weinberger, K. Q., & Chao, W. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00592 · Cited by 204 · see citing works

A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision

Georgiou, T., Liu, Y., Chen, W., & Lew, M. S. (2019) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-019-00183-w · Cited by 194 · see citing works

Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and Outlook

Song, Z., Liu, L., Jia, F., Luo, Y., Jia, C., Zhang, G., Yang, L., & Wang, L. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3439557 · Cited by 199 · see citing works

SegMap: 3D Segment Mapping using Data-Driven Descriptors

Dubé, R., Cramariuc, A., Dugas, D., Nieto, J. I., Siegwart, R., & Cadena, C. (2018) · Robotics: Science and Systems · doi:10.15607/rss.2018.xiv.003 · Cited by 182 · see citing works

Vehicle Tracking and Speed Estimation From Roadside Lidar

Zhang, J., Xiao, W., Coifman, B. A., & Mills, J. P. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3024921 · Cited by 185 · see citing works

SpAGNN: Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data

Casas, S., Gulino, C., Liao, R., & Urtasun, R. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196697 · Cited by 181 · see citing works

A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery

Pyo, J., Duan, H., Baek, S., Kim, M. S., Jeon, T., Kwon, Y. S., Lee, H., & Cho, K. H. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111350 · Cited by 180 · see citing works

A Hierarchical Graph Network for 3D Object Detection on Point Clouds

Chen, J., Lei, B., Song, Q., Ying, H., Chen, D. Z., & Wu, J. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00047 · Cited by 176 · see citing works

Offboard 3D Object Detection from Point Cloud Sequences

Qi, C. R., Zhou, Y., Najibi, M., Sun, P., Vo, K., Deng, B., & Anguelov, D. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00607 · Cited by 176 · see citing works

Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review

Chintakindi, B. M., Hashmi, M. F., Bokde, N. D., & Geem, Z. W. (2020) · Applied Sciences · doi:10.3390/app10093280 · Cited by 177 · see citing works

Deep Fitting Degree Scoring Network for Monocular 3D Object Detection

Liu, L., Lu, J., Xu, C., Tian, Q. C., & Zhou, J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00115 · Cited by 171 · see citing works

RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving

Zeng, Y., Hu, Y., Liu, S., Ye, J., Han, Y., Li, X., & Sun, N. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2852843 · Cited by 183 · see citing works

A General Pipeline for 3D Detection of Vehicles

Du, X., Ang, M. H., Karaman, S., & Rus, D. L. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8461232 · Cited by 161 · see citing works

Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and CountermeasuresPreprint

Sun, J., Cao, Y., Chen, Q. A., & Mao, Z. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.16974 · Cited by 164 · see citing works

Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation

Meyer, G. P., Charland, J., Hegde, D. K., Laddha, A., & Vallespi-Gonzalez, C. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00162 · Cited by 158 · see citing works

RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects

Yang, B., Guo, R. B., Liang, M., Casas, S., & Urtasun, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58523-5_29 · Cited by 150 · see citing works

Triangulation Learning Network: From Monocular to Stereo 3D Object Detection

Qin, Z., Wang, J., & Lu, Y. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00780 · Cited by 134 · see citing works

Towards A Weakly Supervised Framework for 3D Point Cloud Object Detection and Annotation

Meng, Q., Wang, W., Zhou, T., Shen, J., Jia, Y., & Gool, L. V. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3063611 · Cited by 134 · see citing works

Performance and Challenges of 3D Object Detection Methods in Complex Scenes for Autonomous Driving

Wang, K., Zhou, T., Li, X., & Ren, F. (2022) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2022.3213796 · Cited by 136 · see citing works

Weakly Supervised 3D Object Detection from Lidar Point Cloud

Meng, Q., Wang, W., Zhou, T., Shen, J., Gool, L. V., & Dai, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58601-0_31 · Cited by 126 · see citing works

LidarGait: Benchmarking 3D Gait Recognition with Point Clouds

Shen, C., Fan, C., Wu, W. H., Wang, R., Huang, G. Q., & Yu, S. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00108 · Cited by 120 · see citing works

FuseMODNet: Real-Time Camera and LiDAR Based Moving Object Detection for Robust Low-Light Autonomous Driving

Rashed, H., Ramzy, M., Vaquero, V., Sallab, A. E., Sistu, G., & Yogamani, S. K. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00293 · Cited by 116 · see citing works

A State-of-the-Art Analysis of Obstacle Avoidance Methods from the Perspective of an Agricultural Sprayer UAV’s Operation Scenario

Ahmed, S., Qiu, B., Ahmad, F., Kong, C.-W., & Xin, H. (2021) · Agronomy · doi:10.3390/agronomy11061069 · Cited by 114 · see citing works

A Survey on Deep-Learning-Based LiDAR 3D Object Detection for Autonomous Driving

Alaba, S. Y., & Ball, J. E. (2022) · Italian National Conference on Sensors · doi:10.3390/s22249577 · Cited by 124 · see citing works

A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services

Katare, D., Perino, D., Nurmi, J., Warnier, M., Janssen, M., & Ding, A. Y. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3302474 · Cited by 119 · see citing works

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Sighencea, B. I., Stanciu, R., & Căleanu, C. D. (2021) · Sensors · doi:10.3390/s21227543 · Cited by 113 · see citing works

A review on object pose recovery: From 3D bounding box detectors to full 6D pose estimators

Şahin, C., Garcia-Hernando, G., Sock, J., & Kim, T. (2020) · Image and Vision Computing · doi:10.1016/j.imavis.2020.103898 · Cited by 114 · see citing works

Recent Advances in Object Detection in the Age of Deep Convolutional Neural NetworksPreprint

Agarwal, S., du Terrail, J. O., & Jurie, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.03193 · Cited by 133 · see citing works

Semantics for Robotic Mapping, Perception and Interaction: A Survey

Garg, S., Sünderhauf, N., Dayoub, F., Morrison, D., Cosgun, A., Carneiro, G., Wu, Q., Chin, T.-J., Reid, I. D., Gould, S. J., et al. (2020) · Foundations and Trends in Robotics · doi:10.1561/2300000059 · Cited by 114 · see citing works

Complex-YOLO: Real-time 3D Object Detection on Point CloudsPreprint

Simón, M., Milz, S., Amende, K., & Groß, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.06199 · Cited by 120 · see citing works

Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods

Gao, B., Pan, Y., Li, C., Geng, S., & Zhao, H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3076844 · Cited by 105 · see citing works

Uniformizing Techniques to Process CT Scans with 3D CNNs for Tuberculosis Prediction

Zunair, H., Rahman, A., Mohammed, N., & Cohen, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59354-4_15 · Cited by 105 · see citing works

3D Object Detection Using Scale Invariant and Feature Reweighting Networks

Zhao, X., Liu, Z., Hu, R., & Huang, K. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019267 · Cited by 102 · see citing works

LocNet: Global Localization in 3D Point Clouds for Mobile Vehicles

Yin, H., Tang, L., Ding, X., Wang, Y., & Xiong, R. (2018) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2018.8500682 · Cited by 101 · see citing works

Lidar Point Cloud Compression, Processing and Learning for Autonomous Driving

Abbasi, R., Bashir, A. K., Alyamani, H. J., Amin, F., Doh, J., & Chen, J. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3167957 · Cited by 103 · see citing works

FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Data

Krispel, G., Opitz, M., Waltner, G., Possegger, H., & Bischof, H. (2020) · 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv45572.2020.9093584 · Cited by 99 · see citing works

MonoFENet: Monocular 3D Object Detection With Feature Enhancement Networks

Bao, W., Xu, B., & Chen, Z. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2952201 · Cited by 93 · see citing works

CNN for IMU assisted odometry estimation using velodyne LiDAR

Veľas, M., Španěl, M., Hradiš, M., & Herout, A. (2018) · IEEE International Conference on Autonomous Robot Systems and Competitions · doi:10.1109/icarsc.2018.8374163 · Cited by 84 · see citing works

Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector

Feng, D., Wei, X., Rosenbaum, L., Maki, A., & Dietmayer, K. (2019) · 2019 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2019.8814236 · Cited by 85 · see citing works

3D fully convolutional network for vehicle detection in point cloud

Li, B. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8205955 · Cited by 488 · see citing works

DepthCN: Vehicle detection using 3D-LIDAR and ConvNet

Asvadi, A., Garrote, L., Premebida, C., Peixoto, P., & Nunes, U. (2017) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2017.8317880 · Cited by 76 · see citing works

Deconvolutional networks for point-cloud vehicle detection and tracking in driving scenarios

Vaquero, V., del Pino, I., Moreno-Noguer, F., Solà, J., Sanfeliu, A., & Andrade‐Cetto, J. (2017) · European Conference on Mobile Robots · doi:10.1109/ecmr.2017.8098657 · Cited by 39 · see citing works

Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images

Tian, Z., Chu, X., Wang, X., Wei, X., & Shen, C. (2022) · neural information processing systems · doi:10.52202/068431-2529 · Cited by 71 · see citing works

Image guidance based 3D vehicle detection in traffic scene

Dai, D., Wang, J., Chen, Z., & Zhao, H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.11.060 · Cited by 20 · see citing works