Recent research towards 3D reconstruction has delivered reliable and fast pipelines to obtain accurate volumetric maps of large environments. Alongside, we witness dramatic improvements in the field of semantic segmentation of images due to deployment of deep learning architectures. In this paper, we pursue bridging the semantic gap of purely geometric representations by leveraging on a SLAM pipeline and a deep neural network so to endow surface patches with category labels. In particular, we present the first system that, based on the input stream provided by a commodity RGB-D sensor, can deliver interactively and automatically a map of a large scale environment featuring both geometric as well as semantic information. We also show how the significant computational cost inherent to deployment of a state-of-the-art deep network for semantic labeling does not hinder interactivity thanks to suitable scheduling of the workload on an off-the-shelf PC platform equipped with two GPUs.

Cavallari, T., Di Stefano, L. (2016). On-line large scale semantic fusion. Springer Verlag [10.1007/978-3-319-49409-8_10].

On-line large scale semantic fusion

CAVALLARI, TOMMASO;DI STEFANO, LUIGI
2016

Abstract

Recent research towards 3D reconstruction has delivered reliable and fast pipelines to obtain accurate volumetric maps of large environments. Alongside, we witness dramatic improvements in the field of semantic segmentation of images due to deployment of deep learning architectures. In this paper, we pursue bridging the semantic gap of purely geometric representations by leveraging on a SLAM pipeline and a deep neural network so to endow surface patches with category labels. In particular, we present the first system that, based on the input stream provided by a commodity RGB-D sensor, can deliver interactively and automatically a map of a large scale environment featuring both geometric as well as semantic information. We also show how the significant computational cost inherent to deployment of a state-of-the-art deep network for semantic labeling does not hinder interactivity thanks to suitable scheduling of the workload on an off-the-shelf PC platform equipped with two GPUs.
2016
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
83
99
Cavallari, T., Di Stefano, L. (2016). On-line large scale semantic fusion. Springer Verlag [10.1007/978-3-319-49409-8_10].
Cavallari, Tommaso; Di Stefano, Luigi
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/589909
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact