Mobile 3D object detection in clutter

This paper presents a method for multi-view 3D robotic object recognition targeted for cluttered indoor scenes. We explicitly model occlusions that cause failures in visual detectors by learning a generative appearance-occlusion model from a training set containing annotated 3D objects, images and point clouds. A Bayesian 3D object likelihood incorporates visual information from many views as well as geometric priors for object size and position. An iterative, sampling-based inference technique determines object locations based on the model. We also contribute a novel robot-collected data set with images and point clouds from multiple views of 60 scenes, with over 600 manually annotated 3D objects accounting for over ten thousand bounding boxes. This data has been released to the community. Our results show that our system is able to robustly recognize objects in realistic scenes, significantly improving recognition performance in clutter.

Authors

• David Meger
• James J. Little

Keywords

• Three dimensional displays
• Visualization
• Detectors
• Robots
• Solid modeling
• Geometry
• Computational modeling
• Object Detection
• Point Cloud
• Bounding Box
• 3D Point
• Object Position
• Inference Procedure
• Objects In The Scene
• Recognizable
• Free Space
• Testing Protocol
• Number Of Objects
• Local Image
• Image Space
• 3D Volume
• Visual Object
• Detector Set
• 3D Position
• Category Labels
• Iterative Refinement
• Point Cloud Data
• 3D Region
• Object Appearance
• True Objective
• Object Volume
• Image Appearance
• Gradient Ascent
• Alignment Errors
• Depth Camera
• Mobile Platform
• Specific Instances