# A Coarse-to-Fine Model for 3D Pose Estimation and Sub-category Recognition

arXiv: https://arxiv.org/abs/1504.02764 · HTML: https://mlsanity.com/paper/1504.02764/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2015.

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

- [PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection](https://mlsanity.com/paper/2102.00463/) (2021)
- [PyTorchGeoNodes: Enabling Differentiable Shape Programs for 3D Shape Reconstruction](https://mlsanity.com/paper/2404.10620/) (2024)
- [HOC-Search: Efficient CAD Model and Pose Retrieval from RGB-D Scans](https://mlsanity.com/paper/2309.06107/) (2023)
- [OriCon3D: Effective 3D Object Detection using Orientation and Confidence](https://mlsanity.com/paper/2304.14484/) (2023)

## Abstract (arXiv)

Despite the fact that object detection, 3D pose estimation, and sub-category recognition are highly correlated tasks, they are usually addressed independently from each other because of the huge space of parameters. To jointly model all of these tasks, we propose a coarse-to-fine hierarchical representation, where each level of the hierarchy represents objects at a different level of granularity. The hierarchical representation prevents performance loss, which is often caused by the increase in the number of parameters (as we consider more tasks to model), and the joint modelling enables resolving ambiguities that exist in independent modelling of these tasks. We augment PASCAL3D+ dataset with annotations for these tasks and show that our hierarchical model is effective in joint modelling of object detection, 3D pose estimation, and sub-category recognition.