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.
On-line large scale semantic fusion / Cavallari, Tommaso; Di Stefano, Luigi. - ELETTRONICO. - 9915:(2016), pp. 83-99. (Intervento presentato al convegno 14th European Conference on Computer Vision, ECCV 2016 tenutosi a nld nel 2016) [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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
