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Awesome Monocular 3D detection
| Date | Stars |
|---|---|
| 2026-07-24 | 439 |
| 2026-07-25 | 439 |
| 2026-07-28 | 439 |
| 2026-07-30 | 439 |
| 2026-08-06 | 439 |
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# Awesome Monocular 3D Detection
Paper list of 3D detetction, keep updating!
## Contents
- [Paper List](#Paper-List)
- [2024](#2024)
- [2023](#2023)
- [2022](#2022)
- [2021](#2021)
- [2020](#2020)
- [2019](#2019)
- [2018](#2018)
- [2017](#2017)
- [2016](#2016)
- [KITTI Results](#KITTI-Results)
# Paper List
## 2024
- <a id="MonoWAD"></a>**[MonoWAD]** MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object Detection [[ECCV2024](https://arxiv.org/pdf/2407.16448)][[Pytorch](https://github.com/VisualAIKHU/MonoWAD)]
- <a id="MonoTTA"></a>**[MonoTTA]** Fully Test-Time Adaptation for Monocular 3D Object Detection [[ECCV2024](https://arxiv.org/pdf/2405.19682)][[Pytorch](https://github.com/Hongbin98/MonoTTA)]
- <a id="MonoMAE"></a>**[MonoMAE]** MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders [[NeurIPS2024](https://arxiv.org/pdf/2405.07696)]
- <a id="OVM3D"></a>**[OVM3D]** Training an Open-Vocabulary Monocular 3D Object Detection Model without 3D Data [[NeurIPS2024](https://arxiv.org/pdf/2411.15657)]
- <a id="MonoCD"></a>**[MonoCD]** MonoCD: Monocular 3D Object Detection with Complementary Depths [[CVPR2024](https://arxiv.org/pdf/2404.03181)][[Pytorch](https://github.com/elvintanhust/MonoCD)]
- <a id="DPL"></a>**[DPL]** Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection [[CVPR2024](https://arxiv.org/pdf/2403.17387)]
- <a id="UniMODE"></a>**[UniMODE]** UniMODE: Unified Monocular 3D Object Detection [[CVPR2024](https://arxiv.org/pdf/2402.18573)]
- <a id="YOLOBU"></a>**[YOLOBU]** You Only Look Bottom-Up for Monocular 3D Object Detection [[RA-L2024](https://arxiv.org/pdf/2401.15319)]
## 2023
- <a id="DDML"></a>**[DDML]** Depth-discriminative Metric Learning for Monocular 3D Object Detection [[NeurIPS2023](https://arxiv.org/pdf/2401.01075.pdf)]
- <a id="MonoXiver"></a>**[MonoXiver]** Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver [[ICCV2023](https://arxiv.org/pdf/2304.01289.pdf)]
- <a id="MonoNeRD"></a>**[MonoNeRD]** MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection [[ICCV2023](https://arxiv.org/pdf/2308.09421.pdf)][[Pytorch](https://github.com/cskkxjk/MonoNeRD)]
- <a id="MonoATT"></a>**[MonoATT]** MonoATT: Online Monocular 3D Object Detection with Adaptive Token Transformer [[CVPR2023](https://arxiv.org/abs/2303.13018)]
- <a id="WeakMono3D"></a>**[WeakMono3D]** Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistency [[CVPR2023](https://arxiv.org/pdf/2303.08686.pdf)]
- <a id="MonoPGC"></a>**[MonoPGC]** MonoPGC: Monocular 3D Object Detection with Pixel Geometry Contexts [[ICRA2023](https://arxiv.org/pdf/2302.10549.pdf)]
- <a id="ADD"></a>**[ADD]** Attention-based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection[[AAAI2023](https://arxiv.org/pdf/2211.16779.pdf)]
## 2022
- <a id="MoGDE"></a>**[MoGDE]** MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation [[NeurIPS2022](https://arxiv.org/abs/2303.13561)]
- <a id="LPCG"></a>**[LPCG]** Lidar Point Cloud Guided Monocular 3D Object Detection [[ECCV2022](https://arxiv.org/abs/2104.09035)][[Pytorch](https://github.com/SPengLiang/LPCG)]
- <a id="MVC-MonoDet"></a>**[MVC-MonoDet]** Semi-Supervised Monocular 3D Object Detection by Multi-View Consistency [[ECCV2022](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136680702.pdf)][[Pytorch](https://github.com/lianqing11/mvc_monodet)]
- <a id="CMKD"></a>**[CMKD]** Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection [[ECCV2022](https://arxiv.org/abs/2211.07171)][[Pytorch](https://github.com/Cc-Hy/CMKD)]
- <a id="DfM"></a>**[DfM]** Monocular 3D Object Detection with Depth from Motion [[ECCV2022](https://arxiv.org/pdf/2207.12988.pdf)][[Pytorch](https://github.com/Tai-Wang/Depth-from-Motion)]
- <a id="DEVIANT"></a>**[DEVIANT]** DEVIANT: Depth EquiVarIAnt NeExcerpt of 29,545 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f475fcae02e881f7, topic:computer-vision, readme:object detection, readme:depth estimation