# 3D Pose Estimation for Fine-Grained Object Categories

arXiv: https://arxiv.org/abs/1806.04314 · HTML: https://mlsanity.com/paper/1806.04314/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

## Built on by (most cited first, 6 of 6)

- [Deep Learning for Camera Calibration and Beyond: A Survey](https://mlsanity.com/paper/2303.10559/) (2023)
- [PoseContrast: Class-Agnostic Object Viewpoint Estimation in the Wild with Pose-Aware Contrastive Learning](https://mlsanity.com/paper/2105.05643/) (2021)
- [Focal Length and Object Pose Estimation via Render and Compare](https://mlsanity.com/paper/2204.05145/) (2022)
- [Generalizing Single-View 3D Shape Retrieval to Occlusions and Unseen Objects](https://mlsanity.com/paper/2401.00405/) (2023)
- [FocalPose++: Focal Length and Object Pose Estimation via Render and Compare](https://mlsanity.com/paper/2312.02985/) (2023)
- [ToosiCubix: Monocular 3D Cuboid Labeling via Vehicle Part Annotations](https://mlsanity.com/paper/2506.21358/) (2025)

## Abstract (arXiv)

Existing object pose estimation datasets are related to generic object types and there is so far no dataset for fine-grained object categories. In this work, we introduce a new large dataset to benchmark pose estimation for fine-grained objects, thanks to the availability of both 2D and 3D fine-grained data recently. Specifically, we augment two popular fine-grained recognition datasets (StanfordCars and CompCars) by finding a fine-grained 3D CAD model for each sub-category and manually annotating each object in images with 3D pose. We show that, with enough training data, a full perspective model with continuous parameters can be estimated using 2D appearance information alone. We achieve this via a framework based on Faster/Mask R-CNN. This goes beyond previous works on category-level pose estimation, which only estimate discrete/continuous viewpoint angles or recover rotation matrices often with the help of key points. Furthermore, with fine-grained 3D models available, we incorporate a dense 3D representation named as location field into the CNN-based pose estimation framework to further improve the performance. The new dataset is available at www.umiacs.umd.edu/~wym/3dpose.html