3D Pose Regression Using Convolutional Neural Networks

3D pose estimation is a key component of many important computer vision tasks like autonomous navigation and robot manipulation. Current state-of-the-art approaches for 3D object pose estimation, like Viewpoints & Keypoints and Render for CNN, solve this problem by discretizing the pose space in...

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Bibliographic Details
Published in2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) pp. 494 - 495
Main Authors Mahendran, Siddharth, Ali, Haider, Vidal, Rene
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2017
Subjects
Azimuth
Cameras
Solid modeling
Three-dimensional displays
Training
Two dimensional displays
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Summary:3D pose estimation is a key component of many important computer vision tasks like autonomous navigation and robot manipulation. Current state-of-the-art approaches for 3D object pose estimation, like Viewpoints & Keypoints and Render for CNN, solve this problem by discretizing the pose space into bins and solving a pose-classification task. We argue that 3D pose is continuous and can be solved in a regression framework if done with the right representation, data augmentation and loss function. We modify a standard VGG network for the task of 3D pose regression and show competitive performance compared to state-of-the-art.
ISSN:2160-7516
DOI:10.1109/CVPRW.2017.73