# ePose: Let's Make EfficientPose More Generally Applicable

arXiv: https://arxiv.org/abs/2111.15114 · HTML: https://mlsanity.com/paper/2111.15114/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 13 of 13)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1812.07179/) (2018)
- [3D Bounding Box Estimation Using Deep Learning and Geometry](https://mlsanity.com/paper/1612.00496/) (2016)
- [Real-Time Seamless Single Shot 6D Object Pose Prediction](https://mlsanity.com/paper/1711.08848/) (2017)
- [Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction](https://mlsanity.com/paper/1904.01690/) (2019)
- [Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data](https://mlsanity.com/paper/1812.00287/) (2018)
- [EfficientPose: An efficient, accurate and scalable end-to-end 6D multi object pose estimation approach](https://mlsanity.com/paper/2011.04307/) (2020)
- [BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View](https://mlsanity.com/paper/2003.04188/) (2020)
- [PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points](https://mlsanity.com/paper/1912.07744/) (2019)

## Abstract (arXiv)

EfficientPose is an impressive 3D object detection model. It has been demonstrated to be quick, scalable, and accurate, especially when considering that it uses only RGB inputs. In this paper we try to improve on EfficientPose by giving it the ability to infer an object's size, and by simplifying both the data collection and loss calculations. We evaluated ePose using the Linemod dataset and a new subset of it called "Occlusion 1-class". We also outline our current progress and thoughts about using ePose with the NuScenes and the 2017 KITTI 3D Object Detection datasets. The source code is available at https://github.com/tbd-clip/EfficientPose.