Object recognition through pose and shape estimation

TL;DR

This paper reviews state-of-the-art methods for object pose and shape estimation in computer vision, emphasizing their accuracy, complexity, and performance in gesture and movement recognition tasks.

Contribution

It provides a comprehensive review of existing techniques for object pose and shape estimation, comparing their effectiveness and computational aspects.

Findings

Shape-based pose estimation methods show high accuracy.

Appearance-based techniques are computationally efficient.

Feature comparison approaches balance accuracy and complexity.

Abstract

Computer vision helps machines or computer to see like humans. Computer Takes information from the images and then understands of useful information from images. Gesture recognition and movement recognition are the current area of research in computer vision. For both gesture and movement recognition finding pose of an object is of great importance. The purpose of this paper is to review many state of art which is already available for finding the pose of object based on shape, based on appearance, based on feature and comparison for its accuracy, complexity and performance