# Rotation Matters: Generalized Monocular 3D Object Detection for Various Camera Systems

arXiv: https://arxiv.org/abs/2310.05366 · HTML: https://mlsanity.com/paper/2310.05366/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 9 Oct 2023.

## Builds on (most cited first, 16 of 16)

- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation](https://mlsanity.com/paper/2002.10111/) (2020)
- [Objects are Different: Flexible Monocular 3D Object Detection](https://mlsanity.com/paper/2104.02323/) (2021)
- [CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2011.04841/) (2020)
- [CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection](https://mlsanity.com/paper/2009.00784/) (2020)
- [RangeDet:In Defense of Range View for LiDAR-based 3D Object Detection](https://mlsanity.com/paper/2103.10039/) (2021)
- [Delving into Localization Errors for Monocular 3D Object Detection](https://mlsanity.com/paper/2103.16237/) (2021)
- [ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape](https://mlsanity.com/paper/1812.02781/) (2018)
- [HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching](https://mlsanity.com/paper/2007.12140/) (2020)
- [RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection](https://mlsanity.com/paper/2106.13365/) (2021)
- [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)
- [Monocular 3D Object Detection: An Extrinsic Parameter Free Approach](https://mlsanity.com/paper/2106.15796/) (2021)
- [Homography Loss for Monocular 3D Object Detection](https://mlsanity.com/paper/2204.00754/) (2022)
- [Exploring intermediate representation for monocular vehicle pose estimation](https://mlsanity.com/paper/2011.08464/) (2020)
- [Ground Plane Polling for 6DoF Pose Estimation of Objects on the Road](https://mlsanity.com/paper/1811.06666/) (2018)

## Built on by (most cited first, 3 of 3)

- [SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects](https://mlsanity.com/paper/2403.20318/) (2024)
- [CHARM3R: Towards Unseen Camera Height Robust Monocular 3D Detector](https://mlsanity.com/paper/2508.11185/) (2025)
- [Transfer Learning from Simulated to Real Scenes for Monocular 3D Object Detection](https://mlsanity.com/paper/2408.15637/) (2024)

## Abstract (arXiv)

Research on monocular 3D object detection is being actively studied, and as a result, performance has been steadily improving. However, 3D object detection performance is significantly reduced when applied to a camera system different from the system used to capture the training datasets. For example, a 3D detector trained on datasets from a passenger car mostly fails to regress accurate 3D bounding boxes for a camera mounted on a bus. In this paper, we conduct extensive experiments to analyze the factors that cause performance degradation. We find that changing the camera pose, especially camera orientation, relative to the road plane caused performance degradation. In addition, we propose a generalized 3D object detection method that can be universally applied to various camera systems. We newly design a compensation module that corrects the estimated 3D bounding box location and heading direction. The proposed module can be applied to most of the recent 3D object detection networks. It increases AP3D score (KITTI moderate, IoU > 70%) about 6-to-10-times above the baselines without additional training. Both quantitative and qualitative results show the effectiveness of the proposed method.