# MonoGRNet: A General Framework for Monocular 3D Object Detection

arXiv: https://arxiv.org/abs/2104.08797 · HTML: https://mlsanity.com/paper/2104.08797/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 59 of its 79 references that are in the map.
- First posted on arXiv 18 Apr 2021.

## Builds on (most cited first, 30 of 59)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [Deep Ordinal Regression Network for Monocular Depth Estimation](https://mlsanity.com/paper/1806.02446/) (2018)
- [Frustum PointNets for 3D Object Detection from RGB-D Data](https://mlsanity.com/paper/1711.08488/) (2017)
- [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1812.07179/) (2018)
- [3D Packing for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/1905.02693/) (2019)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [PIXOR: Real-time 3D Object Detection from Point Clouds](https://mlsanity.com/paper/1902.06326/) (2019)
- [STD: Sparse-to-Dense 3D Object Detector for Point Cloud](https://mlsanity.com/paper/1907.10471/) (2019)
- [3D Bounding Box Estimation Using Deep Learning and Geometry](https://mlsanity.com/paper/1612.00496/) (2016)
- [SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again](https://mlsanity.com/paper/1711.10006/) (2017)
- [SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation](https://mlsanity.com/paper/2002.10111/) (2020)
- [Multi-Task Multi-Sensor Fusion for 3D Object Detection](https://mlsanity.com/paper/2012.12397/) (2020)
- [Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB](https://mlsanity.com/paper/1712.03453/) (2017)
- [Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/1906.06310/) (2019)
- [Learning Depth-Guided Convolutions for Monocular 3D Object Detection](https://mlsanity.com/paper/1912.04799/) (2019)

## Built on by (most cited first, 20 of 20)

- [3D Object Detection from Images for Autonomous Driving: A Survey](https://mlsanity.com/paper/2202.02980/) (2022)
- [EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2112.11088/) (2021)
- [BEVHeight++: Toward Robust Visual Centric 3D Object Detection](https://mlsanity.com/paper/2309.16179/) (2023)
- [Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection](https://mlsanity.com/paper/2207.04448/) (2022)
- [MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders](https://mlsanity.com/paper/2405.07696/) (2024)
- [Weakly Supervised Training of Monocular 3D Object Detectors Using Wide Baseline Multi-view Traffic Camera Data](https://mlsanity.com/paper/2110.10966/) (2021)
- [Lite-FPN for Keypoint-based Monocular 3D Object Detection](https://mlsanity.com/paper/2105.00268/) (2021)
- [Unleashing the Power of Chain-of-Prediction for Monocular 3D Object Detection](https://mlsanity.com/paper/2505.04594/) (2025)
- [Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection](https://mlsanity.com/paper/2211.01556/) (2022)
- [TiGDistill-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning Distillation](https://mlsanity.com/paper/2412.20911/) (2024)
- [MVM3Det: A Novel Method for Multi-view Monocular 3D Detection](https://mlsanity.com/paper/2109.10473/) (2021)
- [AA3DNet: Attention Augmented Real Time 3D Object Detection](https://mlsanity.com/paper/2107.12137/) (2021)
- [Joint stereo 3D object detection and implicit surface reconstruction](https://mlsanity.com/paper/2111.12924/) (2021)
- [MonoSKD: General Distillation Framework for Monocular 3D Object Detection via Spearman Correlation Coefficient](https://mlsanity.com/paper/2310.11316/) (2023)
- [PLOT: Pseudo-Labeling via Object Tracking for Monocular 3D Object Detection](https://mlsanity.com/paper/2507.02393/) (2025)
- [IDMS: Instance Depth for Multi-scale Monocular 3D Object Detection](https://mlsanity.com/paper/2212.01528/) (2022)
- [Understanding Depth Map Progressively: Adaptive Distance Interval Separation for Monocular 3d Object Detection](https://mlsanity.com/paper/2306.10921/) (2023)
- [SpikeSMOKE: Spiking Neural Networks for Monocular 3D Object Detection with Cross-Scale Gated Coding](https://mlsanity.com/paper/2506.07737/) (2025)
- [MoCA3D: Monocular 3D Bounding Box Prediction in the Image Plane](https://mlsanity.com/paper/2603.19538/) (2026)
- [XiDepth: a Lightweight and Efficient Network for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2608.03666/) (2026)

## 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.