6D Object Pose Regression via Supervised Learning on Point Clouds

This paper addresses the task of estimating the 6 degrees of freedom pose of a known 3D object from depth information represented by a point cloud. Deep features learned by convolutional neural networks from color information have been the dominant features to be used for inferring object poses, while depth information receives much less attention. However, depth information contains rich geometric information of the object shape, which is important for inferring the object pose. We use depth information represented by point clouds as the input to both deep networks and geometry-based pose refinement and use separate networks for rotation and translation regression. We argue that the axis-angle representation is a suitable rotation representation for deep learning, and use a geodesic loss function for rotation regression. Ablation studies show that these design choices outperform alternatives such as the quaternion representation and L2 loss, or regressing translation and rotation with the same network. Our simple yet effective approach clearly outperforms state-of-the-art methods on the YCB-video dataset.

Authors

• Ge Gao
• Mikko Lauri
• Yulong Wang
• Xiaolin Hu 0001
• Jianwei Zhang 0001
• Simone Frintrop

Keywords

• Three-dimensional displays
• Pose estimation
• Feature extraction
• Image color analysis
• Supervised learning
• Rotation measurement
• Quaternions
• Point Cloud
• Object Pose
• Pose Regression
• 6D Object Pose
• Loss Function
• Deep Learning
• Convolutional Neural Network
• Deep Network
• Feature Learning
• Depth Information
• Geometric Information
• Color Information
• Separate Networks
• L2 Loss
• Random Forest
• Total Loss
• Multilayer Perceptron
• Handcrafted Features
• Point Cloud Segmentation
• Object Segmentation
• Geodesic Distance
• Suitable Choice
• Human Pose Estimation
• Laser Ranging
• Translation Network
• Root Mean Square Error Of Cross-validation
• Class Information