PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

Abstract

Estimating the 6D pose of known objects is important for robots to interact with the real world. The problem is challenging due to the variety of objects as well as the complexity of a scene caused by clutter and occlusions between objects. In this work, we introduce PoseCNN, a new Convolutional Neural Network for 6D object pose estimation. PoseCNN estimates the 3D translation of an object by localizing its center in the image and predicting its distance from the camera. The 3D rotation of the object is estimated by regressing to a quaternion representation. We also introduce a novel loss function that enables PoseCNN to handle symmetric objects. In addition, we contribute a large scale video dataset for 6D object pose estimation named the YCB-Video dataset. Our dataset provides accurate 6D poses of 21 objects from the YCB dataset observed in 92 videos with 133,827 frames. We conduct extensive experiments on our YCB-Video dataset and the OccludedLINEMOD dataset to show that PoseCNN is highly robust to occlusions, can handle symmetric objects, and provide accurate pose estimation using only color images as input. When using depth data to further refine the poses, our approach achieves state-of-the-art results on the challenging OccludedLINEMOD dataset. Our code and dataset are available at https://rse-lab.cs.washington.edu/projects/posecnn/.

Accepted to RSS 2018

References in corpus (8)

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Cited by in corpus (15)

• DeepIM: Deep Iterative Matching for 6D Pose Estimation
• One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning
• 6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping
• Multi-Task Deep Networks for Depth-Based 6D Object Pose and Joint Registration in Crowd Scenarios
• 3D-FUTURE: 3D Furniture shape with TextURE
• Empirical Comparison of Four Stereoscopic Depth Sensing Cameras for Robotics Applications
• Semantic keypoint-based pose estimation from single RGB frames
• Category-Level Articulated Object Pose Estimation
• Robotic Task Success Evaluation Under Multi-modal Non-Parametric Object Pose Uncertainty
• Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
• Active 6D Multi-Object Pose Estimation in Cluttered Scenarios with Deep Reinforcement Learning
• Self-Supervised Object-in-Gripper Segmentation from Robotic Motions
• Deep Positional and Relational Feature Learning for Rotation-Invariant Point Cloud Analysis
• Iterative Optimisation with an Innovation CNN for Pose Refinement
• Recovering 6D Object Pose: A Review and Multi-modal Analysis