# Convolutional Neural Networks for joint object detection and pose estimation: A comparative study

arXiv: https://arxiv.org/abs/1412.7190 · HTML: https://mlsanity.com/paper/1412.7190/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2014.

## Built on by (most cited first, 1 of 1)

- [Semantic keypoint-based pose estimation from single RGB frames](https://mlsanity.com/paper/2204.05864/) (2022)

## Abstract (arXiv)

In this paper we study the application of convolutional neural networks for jointly detecting objects depicted in still images and estimating their 3D pose. We identify different feature representations of oriented objects, and energies that lead a network to learn this representations. The choice of the representation is crucial since the pose of an object has a natural, continuous structure while its category is a discrete variable. We evaluate the different approaches on the joint object detection and pose estimation task of the Pascal3D+ benchmark using Average Viewpoint Precision. We show that a classification approach on discretized viewpoints achieves state-of-the-art performance for joint object detection and pose estimation, and significantly outperforms existing baselines on this benchmark.