# ConvPoseCNN: Dense Convolutional 6D Object Pose Estimation

arXiv: https://arxiv.org/abs/1912.07333 · HTML: https://mlsanity.com/paper/1912.07333/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2019.

## Built on by (most cited first, 7 of 7)

- [MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation](https://mlsanity.com/paper/2110.00464/) (2021)
- [6D Object Pose Estimation using Keypoints and Part Affinity Fields](https://mlsanity.com/paper/2107.02057/) (2021)
- [YOLOPose V2: Understanding and Improving Transformer-based 6D Pose Estimation](https://mlsanity.com/paper/2307.11550/) (2023)
- [ConvPoseCNN2: Prediction and Refinement of Dense 6D Object Poses](https://mlsanity.com/paper/2205.11124/) (2022)
- [Efficient Multi-Object Pose Estimation using Multi-Resolution Deformable Attention and Query Aggregation](https://mlsanity.com/paper/2312.08268/) (2023)
- [FastPoseCNN: Real-Time Monocular Category-Level Pose and Size Estimation Framework](https://mlsanity.com/paper/2406.11063/) (2024)
- [Robust PnP on a Neuromorphic Processor for Object Pose Estimation](https://mlsanity.com/paper/2607.16834/) (2026)

## Abstract (arXiv)

6D object pose estimation is a prerequisite for many applications. In recent years, monocular pose estimation has attracted much research interest because it does not need depth measurements. In this work, we introduce ConvPoseCNN, a fully convolutional architecture that avoids cutting out individual objects. Instead we propose pixel-wise, dense prediction of both translation and orientation components of the object pose, where the dense orientation is represented in Quaternion form. We present different approaches for aggregation of the dense orientation predictions, including averaging and clustering schemes. We evaluate ConvPoseCNN on the challenging YCB-Video Dataset, where we show that the approach has far fewer parameters and trains faster than comparable methods without sacrificing accuracy. Furthermore, our results indicate that the dense orientation prediction implicitly learns to attend to trustworthy, occlusion-free, and feature-rich object regions.