# ConvPoseCNN2: Prediction and Refinement of Dense 6D Object Poses

arXiv: https://arxiv.org/abs/2205.11124 · HTML: https://mlsanity.com/paper/2205.11124/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 15 of 15)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [DeepIM: Deep Iterative Matching for 6D Pose Estimation](https://mlsanity.com/paper/1804.00175/) (2018)
- [BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth](https://mlsanity.com/paper/1703.10896/) (2017)
- [Real-Time Seamless Single Shot 6D Object Pose Prediction](https://mlsanity.com/paper/1711.08848/) (2017)
- [Segmentation-driven 6D Object Pose Estimation](https://mlsanity.com/paper/1812.02541/) (2018)
- [Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimation](https://mlsanity.com/paper/1804.03959/) (2018)
- [Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images](https://mlsanity.com/paper/1508.04546/) (2015)
- [Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image](https://mlsanity.com/paper/1802.10367/) (2018)
- [Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images](https://mlsanity.com/paper/1712.03904/) (2017)
- [Refining 6D Object Pose Predictions using Abstract Render-and-Compare](https://mlsanity.com/paper/1910.03412/) (2019)
- [ConvPoseCNN: Dense Convolutional 6D Object Pose Estimation](https://mlsanity.com/paper/1912.07333/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Novel Convolution and Attention Mechanism-based Model for 6D Object Pose Estimation](https://mlsanity.com/paper/2501.01993/) (2024)

## Abstract (arXiv)

Object pose estimation is a key perceptual capability in robotics. We propose a fully-convolutional extension of the PoseCNN method, which densely predicts object translations and orientations. This has several advantages such as improving the spatial resolution of the orientation predictions -- useful in highly-cluttered arrangements, significant reduction in parameters by avoiding full connectivity, and fast inference. We propose and discuss several aggregation methods for dense orientation predictions that can be applied as a post-processing step, such as averaging and clustering techniques. We demonstrate that our method achieves the same accuracy as PoseCNN on the challenging YCB-Video dataset and provide a detailed ablation study of several variants of our method. Finally, we demonstrate that the model can be further improved by inserting an iterative refinement module into the middle of the network, which enforces consistency of the prediction.