Pose Estimation Based on 3D Models

TL;DR

This paper introduces a pose estimation system utilizing rendered image training sets, a patch-based multi-class classification algorithm, and an iterative method to enhance accuracy, achieving state-of-the-art results.

Contribution

The paper presents a novel pose estimation approach based on rendered images and a patch-based classification algorithm with iterative refinement.

Findings

Achieved state-of-the-art performance on pose estimation tasks.

Developed a patch-based multi-class classification algorithm.

Implemented an iterative approach to improve pose prediction accuracy.

Abstract

In this paper, we proposed a pose estimation system based on rendered image training set, which predicts the pose of objects in real image, with knowledge of object category and tight bounding box. We developed a patch-based multi-class classification algorithm, and an iterative approach to improve the accuracy. We achieved state-of-the-art performance on pose estimation task.