# Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection

arXiv: https://arxiv.org/abs/2205.09373 · HTML: https://mlsanity.com/paper/2205.09373/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 50 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 30 of 37)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Deep Learning for 3D Point Clouds: A Survey](https://mlsanity.com/paper/1912.12033/) (2019)
- [Deep Layer Aggregation](https://mlsanity.com/paper/1707.06484/) (2017)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Categorical Depth Distribution Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2103.01100/) (2021)
- [3D Bounding Box Estimation Using Deep Learning and Geometry](https://mlsanity.com/paper/1612.00496/) (2016)
- [M3D-RPN: Monocular 3D Region Proposal Network for Object Detection](https://mlsanity.com/paper/1907.06038/) (2019)
- [Is Pseudo-Lidar needed for Monocular 3D Object detection?](https://mlsanity.com/paper/2108.06417/) (2021)
- [Disentangling Monocular 3D Object Detection](https://mlsanity.com/paper/1905.12365/) (2019)
- [SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation](https://mlsanity.com/paper/2002.10111/) (2020)
- [Probabilistic and Geometric Depth: Detecting Objects in Perspective](https://mlsanity.com/paper/2107.14160/) (2021)
- [Objects are Different: Flexible Monocular 3D Object Detection](https://mlsanity.com/paper/2104.02323/) (2021)
- [Geometry Uncertainty Projection Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2107.13774/) (2021)
- [MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships](https://mlsanity.com/paper/2003.00504/) (2020)
- [RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving](https://mlsanity.com/paper/2001.03343/) (2020)
- [Delving into Localization Errors for Monocular 3D Object Detection](https://mlsanity.com/paper/2103.16237/) (2021)
- [Accurate Monocular Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving](https://mlsanity.com/paper/1903.11444/) (2019)
- [ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape](https://mlsanity.com/paper/1812.02781/) (2018)
- [SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud](https://mlsanity.com/paper/2104.09804/) (2021)
- [Kinematic 3D Object Detection in Monocular Video](https://mlsanity.com/paper/2007.09548/) (2020)
- [What makes instance discrimination good for transfer learning?](https://mlsanity.com/paper/2006.06606/) (2020)
- [Rethinking Pseudo-LiDAR Representation](https://mlsanity.com/paper/2008.04582/) (2020)
- [Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving](https://mlsanity.com/paper/1904.04620/) (2019)
- [AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection](https://mlsanity.com/paper/2108.11127/) (2021)
- [Geometry-based Distance Decomposition for Monocular 3D Object Detection](https://mlsanity.com/paper/2104.03775/) (2021)
- [MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation](https://mlsanity.com/paper/2103.12605/) (2021)
- [End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection](https://mlsanity.com/paper/2004.03080/) (2020)

## Built on by (most cited first, 30 of 46)

- [Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection](https://mlsanity.com/paper/2210.02443/) (2022)
- [MonoCD: Monocular 3D Object Detection with Complementary Depths](https://mlsanity.com/paper/2404.03181/) (2024)
- [Learning Occupancy for Monocular 3D Object Detection](https://mlsanity.com/paper/2305.15694/) (2023)
- [Densely Constrained Depth Estimator for Monocular 3D Object Detection](https://mlsanity.com/paper/2207.10047/) (2022)
- [MonoDGP: Monocular 3D Object Detection with Decoupled-Query and Geometry-Error Priors](https://mlsanity.com/paper/2410.19590/) (2024)
- [MonoLSS: Learnable Sample Selection For Monocular 3D Detection](https://mlsanity.com/paper/2312.14474/) (2023)
- [Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistency](https://mlsanity.com/paper/2303.08686/) (2023)
- [Quality Matters: Embracing Quality Clues for Robust 3D Multi-Object Tracking](https://mlsanity.com/paper/2208.10976/) (2022)
- [NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization](https://mlsanity.com/paper/2305.17763/) (2023)
- [CARLA Drone: Monocular 3D Object Detection from a Different Perspective](https://mlsanity.com/paper/2408.11958/) (2024)
- [GroupLane: End-to-End 3D Lane Detection with Channel-wise Grouping](https://mlsanity.com/paper/2307.09472/) (2023)
- [Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection](https://mlsanity.com/paper/2403.17387/) (2024)
- [MonoTAKD: Teaching Assistant Knowledge Distillation for Monocular 3D Object Detection](https://mlsanity.com/paper/2404.04910/) (2024)
- [Unleashing the Power of Chain-of-Prediction for Monocular 3D Object Detection](https://mlsanity.com/paper/2505.04594/) (2025)
- [LabelDistill: Label-guided Cross-modal Knowledge Distillation for Camera-based 3D Object Detection](https://mlsanity.com/paper/2407.10164/) (2024)
- [Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver](https://mlsanity.com/paper/2304.01289/) (2023)
- [VisHall3D: Monocular Semantic Scene Completion from Reconstructing the Visible Regions to Hallucinating the Invisible Regions](https://mlsanity.com/paper/2507.19188/) (2025)
- [Predict to Detect: Prediction-guided 3D Object Detection using Sequential Images](https://mlsanity.com/paper/2306.08528/) (2023)
- [Towards Unified 3D Object Detection via Algorithm and Data Unification](https://mlsanity.com/paper/2402.18573/) (2024)
- [BEVUDA: Multi-geometric Space Alignments for Domain Adaptive BEV 3D Object Detection](https://mlsanity.com/paper/2211.17126/) (2022)
- [VoxelFormer: Bird's-Eye-View Feature Generation based on Dual-view Attention for Multi-view 3D Object Detection](https://mlsanity.com/paper/2304.01054/) (2023)
- [ClusterFusion: Leveraging Radar Spatial Features for Radar-Camera 3D Object Detection in Autonomous Vehicles](https://mlsanity.com/paper/2309.03734/) (2023)
- [GUPNet++: Geometry Uncertainty Propagation Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2310.15624/) (2023)
- [Depth Is All You Need for Monocular 3D Detection](https://mlsanity.com/paper/2210.02493/) (2022)
- [Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection](https://mlsanity.com/paper/2207.07933/) (2022)
- [Delving into the Pre-training Paradigm of Monocular 3D Object Detection](https://mlsanity.com/paper/2206.03657/) (2022)
- [AdvMono3D: Advanced Monocular 3D Object Detection with Depth-Aware Robust Adversarial Training](https://mlsanity.com/paper/2309.01106/) (2023)
- [Transfer Learning from Simulated to Real Scenes for Monocular 3D Object Detection](https://mlsanity.com/paper/2408.15637/) (2024)
- [MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Models](https://mlsanity.com/paper/2503.13743/) (2025)
- [StereoDETR: Stereo-based Transformer for 3D Object Detection](https://mlsanity.com/paper/2511.18788/) (2025)

## Abstract (arXiv)

As an inherently ill-posed problem, depth estimation from single images is the most challenging part of monocular 3D object detection (M3OD). Many existing methods rely on preconceived assumptions to bridge the missing spatial information in monocular images, and predict a sole depth value for every object of interest. However, these assumptions do not always hold in practical applications. To tackle this problem, we propose a depth solving system that fully explores the visual clues from the subtasks in M3OD and generates multiple estimations for the depth of each target. Since the depth estimations rely on different assumptions in essence, they present diverse distributions. Even if some assumptions collapse, the estimations established on the remaining assumptions are still reliable. In addition, we develop a depth selection and combination strategy. This strategy is able to remove abnormal estimations caused by collapsed assumptions, and adaptively combine the remaining estimations into a single one. In this way, our depth solving system becomes more precise and robust. Exploiting the clues from multiple subtasks of M3OD and without introducing any extra information, our method surpasses the current best method by more than 20% relatively on the Moderate level of test split in the KITTI 3D object detection benchmark, while still maintaining real-time efficiency.