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Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images

arXiv 1511.02300 ↗First posted 7 Nov 2015cs.CVcited by 111

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Abstract (arXiv)

We focus on the task of amodal 3D object detection in RGB-D images, which aims to produce a 3D bounding box of an object in metric form at its full extent. We introduce Deep Sliding Shapes, a 3D ConvNet formulation that takes a 3D volumetric scene from a RGB-D image as input and outputs 3D object bounding boxes. In our approach, we propose the first 3D Region Proposal Network (RPN) to learn objectness from geometric shapes and the first joint Object Recognition Network (ORN) to extract geometric features in 3D and color features in 2D. In particular, we handle objects of various sizes by training an amodal RPN at two different scales and an ORN to regress 3D bounding boxes. Experiments show that our algorithm outperforms the state-of-the-art by 13.8 in mAP and is 200x faster than the original Sliding Shapes. All source code and pre-trained models will be available at GitHub.

Key facts. Cited by 111 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Nov 2015.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 41; 2022: 43; 2023: 13; 2024: 7; 2025: 5; 2026: 2.

111 papers.

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  1. 2021An End-to-End Transformer Model for 3D Object Detectioncited by 329
  2. 2021PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detectioncited by 232
  3. 2021Self-Supervised Pretraining of 3D Features on any Point-Cloudcited by 217
  4. 2021Group-Free 3D Object Detection via Transformerscited by 215
  5. 2021Offboard 3D Object Detection from Point Cloud Sequencescited by 145
  6. 2021Revisiting Point Cloud Shape Classification with a Simple and Effective Baselinecited by 145
  7. 2021FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimationcited by 144
  8. 2021LiDAR R-CNN: An Efficient and Universal 3D Object Detectorcited by 129
  9. 2021SAT: 2D Semantics Assisted Training for 3D Visual Groundingcited by 124
  10. 2021Improving 3D Object Detection with Channel-wise Transformercited by 108
  11. 20213D Object Detection for Autonomous Driving: A Surveycited by 106
  12. 2022Semantic Abstraction: Open-World 3D Scene Understanding from 2D Vision-Language Modelscited by 95
38 more
  1. 2022Surface Representation for Point Cloudscited by 84
  2. 20213D Semantic Scene Completion: a Surveycited by 74
  3. 2021Category-Level 6D Object Pose Estimation via Cascaded Relation and Recurrent Reconstruction Networkscited by 69
  4. 2021Back-tracing Representative Points for Voting-based 3D Object Detection in Point Cloudscited by 67
  5. 2023Deep Learning on Implicit Neural Representations of Shapescited by 63
  6. 2021Pri3D: Can 3D Priors Help 2D Representation Learning?cited by 61
  7. 2022Motion Inspired Unsupervised Perception and Prediction in Autonomous Drivingcited by 59
  8. 2021Semantic Scene Completion via Integrating Instances and Scene in-the-Loopcited by 55
  9. 20214DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understandingcited by 54
  10. 2022Polar Parametrization for Vision-based Surround-View 3D Detectioncited by 53
  11. 2023PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detectioncited by 53
  12. 2022SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detectioncited by 52
  13. 2021EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detectioncited by 49
  14. 2022NeRF-RPN: A general framework for object detection in NeRFscited by 46
  15. 2021Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Drivingcited by 45
  16. 2022Look Around and Refer: 2D Synthetic Semantics Knowledge Distillation for 3D Visual Groundingcited by 41
  17. 2022RBGNet: Ray-based Grouping for 3D Object Detectioncited by 40
  18. 2021VoRTX: Volumetric 3D Reconstruction With Transformers for Voxelwise View Selection and Fusioncited by 38
  19. 2021RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detectioncited by 37
  20. 2023Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Drivingcited by 37
  21. 20213D Spatial Recognition without Spatially Labeled 3Dcited by 31
  22. 2022Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamicscited by 30
  23. 2021PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Cloudscited by 26
  24. 2022Weakly Aligned Feature Fusion for Multimodal Object Detectioncited by 26
  25. 2024A Survey on Text-guided 3D Visual Grounding: Elements, Recent Advances, and Future Directionscited by 25
  26. 2022A Closer Look at Invariances in Self-supervised Pre-training for 3D Visioncited by 24
  27. 2021ODAM: Object Detection, Association, and Mapping using Posed RGB Videocited by 22
  28. 2022Uni6D: A Unified CNN Framework without Projection Breakdown for 6D Pose Estimationcited by 22
  29. 2022LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformerscited by 21
  30. 2025SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorizationcited by 21
  31. 2021MonoGRNet: A General Framework for Monocular 3D Object Detectioncited by 20
  32. 2022Bridged Transformer for Vision and Point Cloud 3D Object Detectioncited by 20
  33. 2021Monte Carlo Scene Search for 3D Scene Understandingcited by 19
  34. 2021NeRD: Neural 3D Reflection Symmetry Detectorcited by 18
  35. 2021Revisiting 3D Object Detection From an Egocentric Perspectivecited by 18
  36. 2022DArch: Dental Arch Prior-assisted 3D Tooth Instance Segmentationcited by 18
  37. 2022Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancementcited by 17
  38. 2022PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognitioncited by 16

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