Multi-View Matching Network for 6D Pose Estimation
Daniel Mas Montserrat, Jianhang Chen, Qian Lin, Jan P. Allebach,, Edward J. Delp
TL;DR
This paper introduces a novel method for estimating the 6D pose of objects from a single RGB image by matching it with rendered images, aiding applications like augmented reality and robotics.
Contribution
The paper presents a new approach that combines object detection, segmentation, and image rendering to improve 6D pose estimation from monocular images.
Findings
Effective pose estimation from single RGB images
Improved accuracy in object localization and orientation
Compatible with detection and segmentation pipelines
Abstract
Applications that interact with the real world such as augmented reality or robot manipulation require a good understanding of the location and pose of the surrounding objects. In this paper, we present a new approach to estimate the 6 Degree of Freedom (DoF) or 6D pose of objects from a single RGB image. Our approach can be paired with an object detection and segmentation method to estimate, refine and track the pose of the objects by matching the input image with rendered images.
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Image and Object Detection Techniques · Advanced Neural Network Applications