mlsanityData to 7 Oct 2026

MonoGRNet: A General Framework for Monocular 3D Object Detection

arXiv 2104.08797 ↗First posted 18 Apr 2021cs.CVcited by 20

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Abstract (arXiv)

Detecting and localizing objects in the real 3D space, which plays a crucial role in scene understanding, is particularly challenging given only a monocular image due to the geometric information loss during imagery projection. We propose MonoGRNet for the amodal 3D object detection from a monocular image via geometric reasoning in both the observed 2D projection and the unobserved depth dimension. MonoGRNet decomposes the monocular 3D object detection task into four sub-tasks including 2D object detection, instance-level depth estimation, projected 3D center estimation and local corner regression. The task decomposition significantly facilitates the monocular 3D object detection, allowing the target 3D bounding boxes to be efficiently predicted in a single forward pass, without using object proposals, post-processing or the computationally expensive pixel-level depth estimation utilized by previous methods. In addition, MonoGRNet flexibly adapts to both fully and weakly supervised learning, which improves the feasibility of our framework in diverse settings. Experiments are conducted on KITTI, Cityscapes and MS COCO datasets. Results demonstrate the promising performance of our framework in various scenarios.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 59 of its 79 references that are in the map. First posted on arXiv 18 Apr 2021.

Builds on

59 of 79 references.

References · 59

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38 more
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50 most cited of 59 All 59 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 4; 2023: 3; 2024: 2; 2025: 3; 2026: 2.

20 papers.

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  1. 20223D Object Detection from Images for Autonomous Driving: A Surveycited by 66
  2. 2021EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detectioncited by 49
  3. 2023BEVHeight++: Toward Robust Visual Centric 3D Object Detectioncited by 29
  4. 2022Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detectioncited by 23
  5. 2024MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoderscited by 15
  6. 2021Weakly Supervised Training of Monocular 3D Object Detectors Using Wide Baseline Multi-view Traffic Camera Datacited by 11
  7. 2021Lite-FPN for Keypoint-based Monocular 3D Object Detectioncited by 10
  8. 2025Unleashing the Power of Chain-of-Prediction for Monocular 3D Object Detectioncited by 8
  9. 2022Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detectioncited by 5
  10. 2024TiGDistill-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning Distillationcited by 4
  11. 2021MVM3Det: A Novel Method for Multi-view Monocular 3D Detectioncited by 3
  12. 2021AA3DNet: Attention Augmented Real Time 3D Object Detectioncited by 1
8 more
  1. 2021Joint stereo 3D object detection and implicit surface reconstructioncited by 1
  2. 2023MonoSKD: General Distillation Framework for Monocular 3D Object Detection via Spearman Correlation Coefficientcited by 1
  3. 2025PLOT: Pseudo-Labeling via Object Tracking for Monocular 3D Object Detectioncited by 1
  4. 2022IDMS: Instance Depth for Multi-scale Monocular 3D Object Detectioncited by 0
  5. 2023Understanding Depth Map Progressively: Adaptive Distance Interval Separation for Monocular 3d Object Detectioncited by 0
  6. 2025SpikeSMOKE: Spiking Neural Networks for Monocular 3D Object Detection with Cross-Scale Gated Codingcited by 0
  7. 2026MoCA3D: Monocular 3D Bounding Box Prediction in the Image Planecited by 0
  8. 2026XiDepth: a Lightweight and Efficient Network for Self-supervised Monocular Depth Estimationcited by 0