Semantic Reconstruction: Reconstruction of Semantically Segmented 3D Meshes via Volumetric Semantic Fusion
| dc.contributor.author | Jeon, Junho | en_US |
| dc.contributor.author | Jung, Jinwoong | en_US |
| dc.contributor.author | Kim, Jungeon | en_US |
| dc.contributor.author | Lee, Seungyong | en_US |
| dc.contributor.editor | Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes | en_US |
| dc.date.accessioned | 2018-10-07T14:57:43Z | |
| dc.date.available | 2018-10-07T14:57:43Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | Semantic segmentation partitions a given image or 3D model of a scene into semantically meaning parts and assigns predetermined labels to the parts. With well-established datasets, deep networks have been successfully used for semantic segmentation of RGB and RGB-D images. On the other hand, due to the lack of annotated large-scale 3D datasets, semantic segmentation for 3D scenes has not yet been much addressed with deep learning. In this paper, we present a novel framework for generating semantically segmented triangular meshes of reconstructed 3D indoor scenes using volumetric semantic fusion in the reconstruction process. Our method integrates the results of CNN-based 2D semantic segmentation that is applied to the RGB-D stream used for dense surface reconstruction. To reduce the artifacts from noise and uncertainty of single-view semantic segmentation, we introduce adaptive integration for the volumetric semantic fusion and CRF-based semantic label regularization. With these methods, our framework can easily generate a high-quality triangular mesh of the reconstructed 3D scene with dense (i.e., per-vertex) semantic labels. Extensive experiments demonstrate that our semantic segmentation results of 3D scenes achieves the state-of-the-art performance compared to the previous voxel-based and point cloud-based methods. | en_US |
| dc.description.number | 7 | |
| dc.description.sectionheaders | Registration and Reconstruction | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 37 | |
| dc.identifier.doi | 10.1111/cgf.13544 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 25-35 | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.13544 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf13544 | |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | Computing methodologies | |
| dc.subject | Reconstruction | |
| dc.subject | Scene understanding | |
| dc.title | Semantic Reconstruction: Reconstruction of Semantically Segmented 3D Meshes via Volumetric Semantic Fusion | en_US |