mlsanityData to 7 Oct 2026

SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud

arXiv 2103.15396 ↗First posted 29 Mar 2021cs.CVcited by 12

Not on the map. Why?

Abstract (arXiv)

LiDAR-based 3D object detection pushes forward an immense influence on autonomous vehicles. Due to the limitation of the intrinsic properties of LiDAR, fewer points are collected at the objects farther away from the sensor. This imbalanced density of point clouds degrades the detection accuracy but is generally neglected by previous works. To address the challenge, we propose a novel two-stage 3D object detection framework, named SIENet. Specifically, we design the Spatial Information Enhancement (SIE) module to predict the spatial shapes of the foreground points within proposals, and extract the structure information to learn the representative features for further box refinement. The predicted spatial shapes are complete and dense point sets, thus the extracted structure information contains more semantic representation. Besides, we design the Hybrid-Paradigm Region Proposal Network (HP-RPN) which includes multiple branches to learn discriminate features and generate accurate proposals for the SIE module. Extensive experiments on the KITTI 3D object detection benchmark show that our elaborately designed SIENet outperforms the state-of-the-art methods by a large margin.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 37 of its 48 references that are in the map. First posted on arXiv 29 Mar 2021.

Builds on

37 of 48 references.

References · 37

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  3. 2017Mask R-CNNcited by 7,259
  4. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  5. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  6. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  7. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
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  11. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  12. 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
25 more
  1. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  2. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 939
  3. 2016Stacked Hourglass Networks for Human Pose Estimationcited by 913
  4. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 890
  5. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 721
  6. 2019Deep Hough Voting for 3D Object Detection in Point Cloudscited by 648
  7. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  8. 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
  9. 2018PCN: Point Completion Networkcited by 382
  10. 2017Cascaded Pyramid Network for Multi-Person Pose Estimationcited by 341
  11. 2020Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloudcited by 335
  12. 2017Joint 3D Proposal Generation and Object Detection from View Aggregationcited by 325
  13. 2019STD: Sparse-to-Dense 3D Object Detector for Point Cloudcited by 282
  14. 2020Deep Continuous Fusion for Multi-Sensor 3D Object Detectioncited by 263
  15. 2020Multi-Task Multi-Sensor Fusion for 3D Object Detectioncited by 203
  16. 20203D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detectioncited by 176
  17. 2020EPNet: Enhancing Point Features with Image Semantics for 3D Object Detectioncited by 164
  18. 2019Learning Object Bounding Boxes for 3D Instance Segmentation on Point Cloudscited by 156
  19. 2020PF-Net: Point Fractal Network for 3D Point Cloud Completioncited by 146
  20. 2019Fast Point R-CNNcited by 119
  21. 20183D Point Capsule Networkscited by 106
  22. 2017High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inferencecited by 87
  23. 2020Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detectioncited by 31
  24. 20203D IoU-Net: IoU Guided 3D Object Detector for Point Cloudscited by 15
  25. 2020Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformationscited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 5; 2024: 1; 2025: 0; 2026: 1.

12 papers.

  1. 2022Focal Sparse Convolutional Networks for 3D Object Detectioncited by 142
  2. 2021Behind the Curtain: Learning Occluded Shapes for 3D Object Detectioncited by 58
  3. 2021EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detectioncited by 49
  4. 2023M-BEV: Masked BEV Perception for Robust Autonomous Drivingcited by 18
  5. 2024SparseDet: A Simple and Effective Framework for Fully Sparse LiDAR-based 3D Object Detectioncited by 7
  6. 2022ImpDet: Exploring Implicit Fields for 3D Object Detectioncited by 4
  7. 2026SD4R: Sparse-to-Dense Learning for 3D Object Detection with 4D Radarcited by 4
  8. 2023BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmapcited by 2
  9. 20233D objects and scenes classification, recognition, segmentation, and reconstruction using 3D point cloud data: A reviewcited by 2
  10. 20213D Object Detection Combining Semantic and Geometric Features from Point Cloudscited by 1
  11. 2023What You See Is What You Detect: Towards better Object Densification in 3D detectioncited by 1
  12. 2023Are All Point Clouds Suitable for Completion? Weakly Supervised Quality Evaluation Network for Point Cloud Completioncited by 0