mlsanityData to 7 Oct 2026

Densely Constrained Depth Estimator for Monocular 3D Object Detection

arXiv 2207.10047 ↗First posted 20 Jul 2022cs.CVcited by 27

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Abstract (arXiv)

Estimating accurate 3D locations of objects from monocular images is a challenging problem because of lacking depth. Previous work shows that utilizing the object's keypoint projection constraints to estimate multiple depth candidates boosts the detection performance. However, the existing methods can only utilize vertical edges as projection constraints for depth estimation. So these methods only use a small number of projection constraints and produce insufficient depth candidates, leading to inaccurate depth estimation. In this paper, we propose a method that utilizes dense projection constraints from edges of any direction. In this way, we employ much more projection constraints and produce considerable depth candidates. Besides, we present a graph matching weighting module to merge the depth candidates. The proposed method DCD (Densely Constrained Detector) achieves state-of-the-art performance on the KITTI and WOD benchmarks. Code is released at https://github.com/BraveGroup/DCD.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 46 of its 57 references that are in the map. First posted on arXiv 20 Jul 2022.

Builds on

46 of 57 references.

References · 46

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  3. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  4. 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,437
  5. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  6. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  7. 2019Objects as Pointscited by 1,172
  8. 2017Deep Layer Aggregationcited by 436
  9. 2021Categorical Depth Distribution Network for Monocular 3D Object Detectioncited by 326
  10. 2017Relation Networks for Object Detectioncited by 305
  11. 20163D Bounding Box Estimation Using Deep Learning and Geometrycited by 277
  12. 2019M3D-RPN: Monocular 3D Region Proposal Network for Object Detectioncited by 251
34 more
  1. 2021Is Pseudo-Lidar needed for Monocular 3D Object detection?cited by 248
  2. 2020SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimationcited by 202
  3. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  4. 2021Probabilistic and Geometric Depth: Detecting Objects in Perspectivecited by 190
  5. 2021Objects are Different: Flexible Monocular 3D Object Detectioncited by 184
  6. 2017Learning to Find Good Correspondencescited by 176
  7. 2019Learning Depth-Guided Convolutions for Monocular 3D Object Detectioncited by 169
  8. 2021Geometry Uncertainty Projection Network for Monocular 3D Object Detectioncited by 155
  9. 2020MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationshipscited by 153
  10. 2020RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Drivingcited by 150
  11. 2021Delving into Localization Errors for Monocular 3D Object Detectioncited by 138
  12. 2019Monocular 3D Object Detection with Pseudo-LiDAR Point Cloudcited by 113
  13. 2020Rethinking Pseudo-LiDAR Representationcited by 109
  14. 2021Improving 3D Object Detection with Channel-wise Transformercited by 108
  15. 2019Learning Combinatorial Embedding Networks for Deep Graph Matchingcited by 98
  16. 2021Robust Point Cloud Registration Framework Based on Deep Graph Matchingcited by 96
  17. 2017Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular imagecited by 95
  18. 2021AutoShape: Real-Time Shape-Aware Monocular 3D Object Detectioncited by 87
  19. 2021Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detectioncited by 84
  20. 2021Geometry-based Distance Decomposition for Monocular 3D Object Detectioncited by 83
  21. 2021MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagationcited by 82
  22. 2020Deep Graph Matching via Blackbox Differentiation of Combinatorial Solverscited by 81
  23. 20213D-MAN: 3D Multi-frame Attention Network for Object Detectioncited by 73
  24. 2021Monocular 3D Object Detection: An Extrinsic Parameter Free Approachcited by 69
  25. 2021Ground-aware Monocular 3D Object Detection for Autonomous Drivingcited by 65
  26. 2021Progressive Coordinate Transforms for Monocular 3D Object Detectioncited by 52
  27. 2021Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Trackingcited by 47
  28. 2022Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detectioncited by 45
  29. 2019Towards Generalization Across Depth for Monocular 3D Object Detectioncited by 41
  30. 2020Solving the Blind Perspective-n-Point Problem End-To-End With Robust Differentiable Geometric Optimizationcited by 38
  31. 20183D Pose Estimation and 3D Model Retrieval for Objects in the Wildcited by 31
  32. 2022MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detectioncited by 30
  33. 2021Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object Detectioncited by 20
  34. 2021IAFA: Instance-aware Feature Aggregation for 3D Object Detection from a Single Imagecited by 15

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 10; 2024: 10; 2025: 5; 2026: 2.

27 papers.

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  1. 2024Enhancing End-to-End Autonomous Driving with Latent World Modelcited by 161
  2. 2025End-to-End Driving with Online Trajectory Evaluation via BEV World Modelcited by 145
  3. 2024MonoCD: Monocular 3D Object Detection with Complementary Depthscited by 38
  4. 2023MonoNeRD: NeRF-like Representations for Monocular 3D Object Detectioncited by 33
  5. 2023Fully Sparse Fusion for 3D Object Detectioncited by 29
  6. 2024MonoDGP: Monocular 3D Object Detection with Decoupled-Query and Geometry-Error Priorscited by 21
  7. 2023NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localizationcited by 13
  8. 2024Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detectioncited by 11
  9. 2024MonoTAKD: Teaching Assistant Knowledge Distillation for Monocular 3D Object Detectioncited by 10
  10. 20233D Video Object Detection with Learnable Object-Centric Global Optimizationcited by 7
  11. 2023Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceivercited by 7
  12. 2024Multi-View Attentive Contextualization for Multi-View 3D Object Detectioncited by 7
15 more
  1. 2024General Geometry-aware Weakly Supervised 3D Object Detectioncited by 7
  2. 2024Towards Unified 3D Object Detection via Algorithm and Data Unificationcited by 6
  3. 2023GUPNet++: Geometry Uncertainty Propagation Network for Monocular 3D Object Detectioncited by 5
  4. 2024You Only Look Bottom-Up for Monocular 3D Object Detectioncited by 3
  5. 2023Weakly Supervised 3D Object Detection with Multi-Stage Generalizationcited by 2
  6. 2023ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimationcited by 2
  7. 2023Towards Fair and Comprehensive Comparisons for Image-Based 3D Object Detectioncited by 1
  8. 2025AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Featurescited by 1
  9. 2025All You Need for Object Detection: From Pixels, Points, and Prompts to Next-Gen Fusion and Multimodal LLMs/VLMs in Autonomous Vehiclescited by 1
  10. 2025LeAD-M3D: Leveraging Asymmetric Distillation for Real-Time Monocular 3D Detectioncited by 1
  11. 2023Learning Monocular Depth in Dynamic Environment via Context-aware Temporal Attentioncited by 0
  12. 2024Efficient Feature Aggregation and Scale-Aware Regression for Monocular 3D Object Detectioncited by 0
  13. 2025MonoVQD: Monocular 3D Object Detection with Variational Query Denoising and Self-Distillationcited by 0
  14. 2026Mono3DV: Monocular 3D Object Detection with 3D-Aware Bipartite Matching and Variational Query DeNoisingcited by 0
  15. 2026Selective Transfer Learning of Cross-Modality Distillation for Monocular 3D Object Detectioncited by 0