Geometrically Consistent Monocular Metric-Semantic 3D Mapping for Indoor Environments with Transparent and Reflecting Objects
Malik Mohrat,Amiran Berkaev,Alexey Burkov,Sergey Kolyubin,Malik Mohrat,Amiran Berkaev,Alexey Burkov,Sergey Kolyubin
3D mapping is crucial for many applications in robotics and related industries. To build dense high-quality point clouds accurate depth estimation or completion is needed. This paper presents the development of a metric-semantic mapping pipeline based on Deep Neural Networks (DNN) which assures geometrical consistency with enhancements for chal-lenging environments with transparent and reflecting ...
