# Robust 6D Object Pose Estimation with Stochastic Congruent Sets

arXiv: https://arxiv.org/abs/1805.06324 · HTML: https://mlsanity.com/paper/1805.06324/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2018.

## Built on by (most cited first, 3 of 3)

- [StablePose: Learning 6D Object Poses from Geometrically Stable Patches](https://mlsanity.com/paper/2102.09334/) (2021)
- [Exploiting Point-Wise Attention in 6D Object Pose Estimation Based on Bidirectional Prediction](https://mlsanity.com/paper/2308.08518/) (2023)
- [An Efficient Method for Accurate Pose Estimation and Error Correction of Cuboidal Objects](https://mlsanity.com/paper/2505.04962/) (2025)

## Abstract (arXiv)

Object pose estimation is frequently achieved by first segmenting an RGB image and then, given depth data, registering the corresponding point cloud segment against the object's 3D model. Despite the progress due to CNNs, semantic segmentation output can be noisy, especially when the CNN is only trained on synthetic data. This causes registration methods to fail in estimating a good object pose. This work proposes a novel stochastic optimization process that treats the segmentation output of CNNs as a confidence probability. The algorithm, called Stochastic Congruent Sets (StoCS), samples pointsets on the point cloud according to the soft segmentation distribution and so as to agree with the object's known geometry. The pointsets are then matched to congruent sets on the 3D object model to generate pose estimates. StoCS is shown to be robust on an APC dataset, despite the fact the CNN is trained only on synthetic data. In the YCB dataset, StoCS outperforms a recent network for 6D pose estimation and alternative pointset matching techniques.