Multimodal blending for high-accuracy instance recognition

TLDR: This work examines the benefits of dense feature extraction and multimodal features for improving the accuracy and robustness of an instance recognition system and obtains significant improvements over previously published results on two RGB-D datasets.

Abstract: Despite the rich information provided by sensors such as the Microsoft Kinect in the robotic perception setting, the problem of detecting object instances remains unsolved, even in the tabletop setting, where segmentation is greatly simplified. Existing object detection systems often focus on textured objects, for which local feature descriptors can be used to reliably obtain correspondences between different views of the same object. We examine the benefits of dense feature extraction and multimodal features for improving the accuracy and robustness of an instance recognition system. By combining multiple modalities and blending their scores through an ensemble-based method in order to generate our final object hypotheses, we obtain significant improvements over previously published results on two RGB-D datasets. On the Challenge dataset, our method results in only one missed detection (achieving 100% precision and 99.77% recall). On the Willow dataset, we also make significant gains on the prior state of the art (achieving 98.28% precision and 87.78% recall), resulting in an increase in F-score from 0.8092 to 0.9273.