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Found 6 papers, 2 papers with code
Monocular Object Orientation Estimation using Riemannian Regression and Classification Networks
We consider the task of estimating the 3D orientation of an object of known category given an image of the object and a bounding box around it.
A Mixed Classification-Regression Framework for 3D Pose Estimation from 2D Images
Since 3D pose is a continuous quantity, a natural formulation for this task is to solve a pose regression problem.
Convolutional Networks for Object Category and 3D Pose Estimation from 2D Images
In this paper, we relax one of these constraints and propose to solve the task of joint object category and 3D pose estimation from an image assuming known 2D localization.
3D Pose Regression using Convolutional Neural Networks
3D pose estimation is a key component of many important computer vision tasks such as autonomous navigation and 3D scene understanding.
Car Segmentation and Pose Estimation using 3D Object Models
Image segmentation and 3D pose estimation are two key cogs in any algorithm for scene understanding.
Understanding Objects in Detail with Fine-Grained Attributes
We show that the collected data can be used to study the relation between part detection and attribute prediction by diagnosing the performance of classifiers that pool information from different parts of an object.
