Persistent Learning for Semantic Indoor Mapping in Dynamic Environments

Kurzfassung

Leveraging deep learning, we propose in this work an approach to tackle the problem of 3D scene reconstruction from single color images in indoor spaces. We rely here on synthetic data and show how to tackle the hard problem of sim2real transfer. Besides a 3D scene reconstruction, we further provide a semantic segmentation of the current viewport.