# Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography

arXiv: https://arxiv.org/abs/2103.15293 · HTML: https://mlsanity.com/paper/2103.15293/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 22 of 22)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1812.07179/) (2018)
- [Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations](https://mlsanity.com/paper/2012.09988/) (2020)
- [3D-LaneNet: End-to-End 3D Multiple Lane Detection](https://mlsanity.com/paper/1811.10203/) (2018)
- [Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image](https://mlsanity.com/paper/1703.07570/) (2017)
- [MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization](https://mlsanity.com/paper/1811.10247/) (2018)
- [A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird's Eye View](https://mlsanity.com/paper/2005.04078/) (2020)
- [A*3D Dataset: Towards Autonomous Driving in Challenging Environments](https://mlsanity.com/paper/1909.07541/) (2019)
- [CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis](https://mlsanity.com/paper/1809.05782/) (2018)
- [Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors](https://mlsanity.com/paper/1901.03446/) (2019)
- [A survey of advances in vision-based vehicle re-identification](https://mlsanity.com/paper/1905.13258/) (2019)
- [Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning](https://mlsanity.com/paper/2008.04047/) (2020)
- [Understanding Traffic Density from Large-Scale Web Camera Data](https://mlsanity.com/paper/1703.05868/) (2017)
- [Traffic Surveillance Camera Calibration by 3D Model Bounding Box Alignment for Accurate Vehicle Speed Measurement](https://mlsanity.com/paper/1702.06451/) (2017)
- [Comprehensive Data Set for Automatic Single Camera Visual Speed Measurement](https://mlsanity.com/paper/1702.06441/) (2017)
- [Detection of 3D Bounding Boxes of Vehicles Using Perspective Transformation for Accurate Speed Measurement](https://mlsanity.com/paper/2003.13137/) (2020)

## Built on by (most cited first, 21 of 21)

- [Cross-view Transformers for real-time Map-view Semantic Segmentation](https://mlsanity.com/paper/2205.02833/) (2022)
- [CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers](https://mlsanity.com/paper/2207.02202/) (2022)
- [Vision-Centric BEV Perception: A Survey](https://mlsanity.com/paper/2208.02797/) (2022)
- [Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe](https://mlsanity.com/paper/2209.05324/) (2022)
- [X-Align: Cross-Modal Cross-View Alignment for Bird's-Eye-View Segmentation](https://mlsanity.com/paper/2210.06778/) (2022)
- [MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware Embeddings](https://mlsanity.com/paper/2310.00400/) (2023)
- [Residual Graph Convolutional Network for Bird's-Eye-View Semantic Segmentation](https://mlsanity.com/paper/2312.04044/) (2023)
- [BroadBEV: Collaborative LiDAR-camera Fusion for Broad-sighted Bird's Eye View Map Construction](https://mlsanity.com/paper/2309.11119/) (2023)
- [One Training for Multiple Deployments: Polar-based Adaptive BEV Perception for Autonomous Driving](https://mlsanity.com/paper/2304.00525/) (2023)
- [The Interstate-24 3D Dataset: a new benchmark for 3D multi-camera vehicle tracking](https://mlsanity.com/paper/2308.14833/) (2023)
- [BEVSeg2TP: Surround View Camera Bird's-Eye-View Based Joint Vehicle Segmentation and Ego Vehicle Trajectory Prediction](https://mlsanity.com/paper/2312.13081/) (2023)
- [You Only Look Bottom-Up for Monocular 3D Object Detection](https://mlsanity.com/paper/2401.15319/) (2024)
- [FishBEV: Distortion-Resilient Bird's Eye View Segmentation with Surround-View Fisheye Cameras](https://mlsanity.com/paper/2509.13681/) (2025)
- [Online Camera-to-ground Calibration for Autonomous Driving](https://mlsanity.com/paper/2303.17137/) (2023)
- [X-Align++: cross-modal cross-view alignment for Bird's-eye-view segmentation](https://mlsanity.com/paper/2306.03810/) (2023)
- [HSDA: High-frequency Shuffle Data Augmentation for Bird's-Eye-View Map Segmentation](https://mlsanity.com/paper/2412.06127/) (2024)
- [2.5D Object Detection for Intelligent Roadside Infrastructure](https://mlsanity.com/paper/2507.03564/) (2025)
- [A Pedestrian-Vehicle Interaction Benchmark and Annotation Framework for Unstructured Scenes via Uncalibrated Cameras](https://mlsanity.com/paper/2605.25947/) (2026)
- [Variational Inference for Bird's Eye View Segmentation in Autonomous Driving](https://mlsanity.com/paper/2607.14710/) (2026)
- [Accurate Localization of Road Traffic Objects on the Road Plane Using Surveillance Camera Imagery](https://mlsanity.com/paper/2608.05840/) (2026)
- [Infrastructure-based Monocular 3D Vehicle Localization Framework with Experimental Validation](https://mlsanity.com/paper/2609.05523/) (2026)

## Abstract (arXiv)

This paper proposes a method to extract the position and pose of vehicles in the 3D world from a single traffic camera. Most previous monocular 3D vehicle detection algorithms focused on cameras on vehicles from the perspective of a driver, and assumed known intrinsic and extrinsic calibration. On the contrary, this paper focuses on the same task using uncalibrated monocular traffic cameras. We observe that the homography between the road plane and the image plane is essential to 3D vehicle detection and the data synthesis for this task, and the homography can be estimated without the camera intrinsics and extrinsics. We conduct 3D vehicle detection by estimating the rotated bounding boxes (r-boxes) in the bird's eye view (BEV) images generated from inverse perspective mapping. We propose a new regression target called tailed r-box and a dual-view network architecture which boosts the detection accuracy on warped BEV images. Experiments show that the proposed method can generalize to new camera and environment setups despite not seeing imaged from them during training.