# Probabilistic Category-Level Pose Estimation via Segmentation and Predicted-Shape Priors

arXiv: https://arxiv.org/abs/1905.12079 · HTML: https://mlsanity.com/paper/1905.12079/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2019.

## Built on by (most cited first, 4 of 4)

- [Category-Level 6D Object Pose Estimation via Cascaded Relation and Recurrent Reconstruction Networks](https://mlsanity.com/paper/2108.08755/) (2021)
- [Optimal Pose and Shape Estimation for Category-level 3D Object Perception](https://mlsanity.com/paper/2104.08383/) (2021)
- [Optimal and Robust Category-level Perception: Object Pose and Shape Estimation from 2D and 3D Semantic Keypoints](https://mlsanity.com/paper/2206.12498/) (2022)
- [Universal Features Guided Zero-Shot Category-Level Object Pose Estimation](https://mlsanity.com/paper/2501.02831/) (2025)

## Abstract (arXiv)

We introduce a new method for category-level pose estimation which produces a distribution over predicted poses by integrating 3D shape estimates from a generative object model with segmentation information. Given an input depth-image of an object, our variable-time method uses a mixture density network architecture to produce a multi-modal distribution over 3DOF poses; this distribution is then combined with a prior probability encouraging silhouette agreement between the observed input and predicted object pose. Our approach significantly outperforms the current state-of-the-art in category-level 3DOF pose estimation---which outputs a point estimate and does not explicitly incorporate shape and segmentation information---as measured on the Pix3D and ShapeNet datasets.