mlsanityData to 7 Oct 2026

SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud

arXiv 2104.09804 ↗First posted 20 Apr 2021cs.CVcited by 119

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Abstract (arXiv)

We present Self-Ensembling Single-Stage object Detector (SE-SSD) for accurate and efficient 3D object detection in outdoor point clouds. Our key focus is on exploiting both soft and hard targets with our formulated constraints to jointly optimize the model, without introducing extra computation in the inference. Specifically, SE-SSD contains a pair of teacher and student SSDs, in which we design an effective IoU-based matching strategy to filter soft targets from the teacher and formulate a consistency loss to align student predictions with them. Also, to maximize the distilled knowledge for ensembling the teacher, we design a new augmentation scheme to produce shape-aware augmented samples to train the student, aiming to encourage it to infer complete object shapes. Lastly, to better exploit hard targets, we design an ODIoU loss to supervise the student with constraints on the predicted box centers and orientations. Our SE-SSD attains top performance compared with all prior published works. Also, it attains top precisions for car detection in the KITTI benchmark (ranked 1st and 2nd on the BEV and 3D leaderboards, respectively) with an ultra-high inference speed. The code is available at https://github.com/Vegeta2020/SE-SSD.

Key facts. Cited by 119 papers in the map (data through 7 Oct 2026). Builds on 33 of its 39 references that are in the map. First posted on arXiv 20 Apr 2021.

Builds on

33 of 39 references.

References · 33

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  3. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  4. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  5. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  6. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  7. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  8. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 939
  9. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 890
  10. 20173D Semantic Segmentation with Submanifold Sparse Convolutional Networkscited by 746
  11. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 721
  12. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
21 more
  1. 2017Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning resultscited by 470
  2. 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 454
  3. 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
  4. 2019From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Networkcited by 366
  5. 2020Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloudcited by 335
  6. 2017Joint 3D Proposal Generation and Object Detection from View Aggregationcited by 325
  7. 2019STD: Sparse-to-Dense 3D Object Detector for Point Cloudcited by 282
  8. 2020Deep Continuous Fusion for Multi-Sensor 3D Object Detectioncited by 263
  9. 2020Multi-Task Multi-Sensor Fusion for 3D Object Detectioncited by 203
  10. 20203D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detectioncited by 176
  11. 2020CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detectioncited by 147
  12. 2019IoU Loss for 2D/3D Object Detectioncited by 128
  13. 2019Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detectioncited by 124
  14. 2019Fast Point R-CNNcited by 119
  15. 2019TANet: Robust 3D Object Detection from Point Clouds with Triple Attentioncited by 108
  16. 2020CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloudcited by 107
  17. 2019SESS: Self-Ensembling Semi-Supervised 3D Object Detectioncited by 89
  18. 2019Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspotscited by 84
  19. 2019PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Modulecited by 56
  20. 2020Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detectioncited by 31
  21. 2020Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformationscited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 49; 2023: 30; 2024: 19; 2025: 10; 2026: 1.

119 papers.

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  1. 2022Focal Sparse Convolutional Networks for 3D Object Detectioncited by 142
  2. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  3. 2023Spherical Transformer for LiDAR-based 3D Recognitioncited by 124
  4. 20213D Object Detection for Autonomous Driving: A Surveycited by 106
  5. 2022PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detectioncited by 99
  6. 2022Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completioncited by 82
  7. 2023Virtual Sparse Convolution for Multimodal 3D Object Detectioncited by 81
  8. 2023LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusioncited by 80
  9. 2022Point Density-Aware Voxels for LiDAR 3D Object Detectioncited by 70
  10. 2022Motion Inspired Unsupervised Perception and Prediction in Autonomous Drivingcited by 59
  11. 2022GD-MAE: Generative Decoder for MAE Pre-training on LiDAR Point Cloudscited by 57
  12. 2022Transformation-Equivariant 3D Object Detection for Autonomous Drivingcited by 52
38 more
  1. 2021EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detectioncited by 49
  2. 2021VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusioncited by 48
  3. 2022Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detectioncited by 48
  4. 2022Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detectioncited by 45
  5. 2022Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graphcited by 42
  6. 2024GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformercited by 42
  7. 2022RBGNet: Ray-based Grouping for 3D Object Detectioncited by 40
  8. 2023Leveraging Hidden Positives for Unsupervised Semantic Segmentationcited by 40
  9. 2022DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detectioncited by 39
  10. 2023Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Drivingcited by 37
  11. 2023Learning Occupancy for Monocular 3D Object Detectioncited by 35
  12. 2022PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detectioncited by 33
  13. 2022CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detectioncited by 32
  14. 2023Super Sparse 3D Object Detectioncited by 32
  15. 2023Density-Insensitive Unsupervised Domain Adaption on 3D Object Detectioncited by 31
  16. 2023SA-BEV: Generating Semantic-Aware Bird's-Eye-View Feature for Multi-view 3D Object Detectioncited by 30
  17. 2022GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimationcited by 24
  18. 2023Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detectioncited by 24
  19. 2023Advancements in Point Cloud Data Augmentation for Deep Learning: A Surveycited by 24
  20. 2022Bootstrap Motion Forecasting With Self-Consistent Constraintscited by 23
  21. 2022Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detectioncited by 23
  22. 2022Boosting 3D Object Detection by Simulating Multimodality on Point Cloudscited by 22
  23. 2023Object Detection with Transformers: A Reviewcited by 22
  24. 2023PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformercited by 21
  25. 2022Rethinking IoU-based Optimization for Single-stage 3D Object Detectioncited by 19
  26. 2022WeakM3D: Towards Weakly Supervised Monocular 3D Object Detectioncited by 18
  27. 2023DDS3D: Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object Detectioncited by 18
  28. 2022Dense Voxel Fusion for 3D Object Detectioncited by 17
  29. 2022Out-of-Distribution Detection for LiDAR-based 3D Object Detectioncited by 17
  30. 2022HyperDet3D: Learning a Scene-conditioned 3D Object Detectorcited by 14
  31. 2022itKD: Interchange Transfer-based Knowledge Distillation for 3D Object Detectioncited by 14
  32. 2022Sparse2Dense: Learning to Densify 3D Features for 3D Object Detectioncited by 14
  33. 2023A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Servicescited by 14
  34. 2024Self-Ensembling Gaussian Splatting for Few-Shot Novel View Synthesiscited by 14
  35. 2021SGM3D: Stereo Guided Monocular 3D Object Detectioncited by 13
  36. 2023Unsupervised Adaptation from Repeated Traversals for Autonomous Drivingcited by 13
  37. 2024Commonsense Prototype for Outdoor Unsupervised 3D Object Detectioncited by 13
  38. 2023Unleash the Potential of Image Branch for Cross-modal 3D Object Detectioncited by 11

50 most cited of 119 All 119 (JSON)