Recent progress in computer vision has been driven by high-capacity deep convolutional neural network (CNN) models trained on generic large datasets. However, creating large datasets with dense pixel-level labels is extremely costly. In this paper, we focus on the problem of instance segmentation for robotic manipulation using rich image and depth features. To avoid intensive human labeling, we develop an automated rendering pipeline for rapidly generating labeled datasets. Given 3D object models as input, the rendering pipeline produces photorealistic images with pixel-accurate semantic label maps and depth maps. The synthetic dataset is then used to train an RGB-D segmentation model by extending the Mask R-CNN framework for depth input fusion. Our results open up new possibilities for advancing robotic perception using cheap and large-scale synthetic data.


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    Title :

    Learning Accurate Objectness Instance Segmentation from Photorealistic Rendering for Robotic Manipulation


    Additional title:

    Springer Proceedings in Advanced Robotics


    Contributors:

    Conference:

    International Symposium on Experimental Robotics ; 2018 ; Buenos Aires, Argentina November 05, 2018 - November 08, 2018



    Publication date :

    2020-01-23


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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