# StereoDETR: Stereo-based Transformer for 3D Object Detection

arXiv: https://arxiv.org/abs/2511.18788 · HTML: https://mlsanity.com/paper/2511.18788/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 48 references that are in the map.
- First posted on arXiv 24 Nov 2025.

## Builds on (most cited first, 30 of 32)

- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection](https://mlsanity.com/paper/2104.10956/) (2021)
- [Virtual Worlds as Proxy for Multi-Object Tracking Analysis](https://mlsanity.com/paper/1605.06457/) (2016)
- [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)
- [Stereo R-CNN based 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1902.09738/) (2019)
- [Geometry Uncertainty Projection Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2107.13774/) (2021)
- [MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer](https://mlsanity.com/paper/2203.10981/) (2022)
- [DSGN: Deep Stereo Geometry Network for 3D Object Detection](https://mlsanity.com/paper/2001.03398/) (2020)
- [MonoDETR: Depth-guided Transformer for Monocular 3D Object Detection](https://mlsanity.com/paper/2203.13310/) (2022)
- [DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection](https://mlsanity.com/paper/2207.10758/) (2022)
- [Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection](https://mlsanity.com/paper/2205.09373/) (2022)
- [Triangulation Learning Network: from Monocular to Stereo 3D Object Detection](https://mlsanity.com/paper/1906.01193/) (2019)
- [DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection](https://mlsanity.com/paper/2207.08531/) (2022)
- [YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection](https://mlsanity.com/paper/2103.09422/) (2021)
- [MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection](https://mlsanity.com/paper/2203.08563/) (2022)
- [Object-Centric Stereo Matching for 3D Object Detection](https://mlsanity.com/paper/1909.07566/) (2019)
- [LightStereo: Channel Boost Is All You Need for Efficient 2D Cost Aggregation](https://mlsanity.com/paper/2406.19833/) (2024)
- [MonoDGP: Monocular 3D Object Detection with Decoupled-Query and Geometry-Error Priors](https://mlsanity.com/paper/2410.19590/) (2024)
- [Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object Detection](https://mlsanity.com/paper/2107.13931/) (2021)
- [RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving](https://mlsanity.com/paper/2012.15072/) (2020)
- [SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimation](https://mlsanity.com/paper/2108.09663/) (2021)
- [Stereo CenterNet based 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2103.11071/) (2021)
- [SSD-MonoDETR: Supervised Scale-aware Deformable Transformer for Monocular 3D Object Detection](https://mlsanity.com/paper/2305.07270/) (2023)
- [ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection](https://mlsanity.com/paper/2111.14055/) (2021)
- [StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection](https://mlsanity.com/paper/2301.01615/) (2023)
- [MonoEdge: Monocular 3D Object Detection Using Local Perspectives](https://mlsanity.com/paper/2301.01802/) (2023)

## Built on by (most cited first, 2 of 2)

- [DDStereo: Efficient Dual Decoder Transformers for Stereo 3D Road Anomaly Detection](https://mlsanity.com/paper/2606.24805/) (2026)
- [Co-Fusion4D: Spatio-temporal Collaborative Fusion for Robust 3D Object Detection](https://mlsanity.com/paper/2605.20301/) (2026)

## Abstract (arXiv)

Compared to monocular 3D object detection, stereo-based 3D methods offer significantly higher accuracy but still suffer from high computational overhead and latency. The state-of-the-art stereo 3D detection method achieves twice the accuracy of monocular approaches, yet its inference speed is only half as fast. In this paper, we propose StereoDETR, an efficient stereo 3D object detection framework based on DETR. StereoDETR consists of two branches: a monocular DETR branch and a stereo branch. The DETR branch is built upon 2D DETR with additional channels for predicting object scale, orientation, and sampling points. The stereo branch leverages low-cost multi-scale disparity features to predict object-level depth maps. These two branches are coupled solely through a differentiable depth sampling strategy. To handle occlusion, we introduce a constrained supervision strategy for sampling points without requiring extra annotations. StereoDETR achieves real-time inference and is the first stereo-based method to surpass monocular approaches in speed. It also achieves competitive accuracy on the public KITTI benchmark, setting new state-of-the-art results on pedestrian and cyclist subsets. The code is available at https://github.com/shiyi-mu/StereoDETR-OPEN.