Algorithm

Pose

Engineering

Statistics

Systems engineering

Computer science

Outlier

Estimation

Unitary representation

Regression

Pattern recognition (psychology)

Mathematics

Politics

Political science

Object (grammar)

Enhanced Data Rates for GSM Evolution

Unitary state

3D pose estimation

Artificial intelligence

Representation (politics)

Artificial neural network

Computer vision

State (computer science)

We introduce HybridPose, a novel 6D object pose estimation approach. HybridPose utilizes a hybrid intermediate representation to express different geometric information in the input image, including keypoints, edge vectors, and symmetry correspondences. Compared to a unitary representation, our hybrid representation allows pose regression to exploit more and diverse features when one type of predicted representation is inaccurate (e.g., because of occlusion). Different intermediate representations used by HybridPose can all be predicted by the same simple neural network, and outliers in predicted intermediate representations are filtered by a robust regression module. Compared to state-of-the-art pose estimation approaches, HybridPose is comparable in running time and is significantly more accurate. For example, on Occlusion Linemod dataset, our method achieves a prediction speed of 30 fps with a mean ADD(-S) accuracy of 79.2%, representing a 67.4% improvement from the current state-of-the-art approach.

HybridPose: 6D Object Pose Estimation Under Hybrid Representations