DC Field | Value | Language |
---|---|---|
dc.contributor.author | Na, Kiin | ko |
dc.contributor.author | Park, Byungjae | ko |
dc.contributor.author | Kim, Jong-Hwan | ko |
dc.date.accessioned | 2020-11-30T12:50:17Z | - |
dc.date.available | 2020-11-30T12:50:17Z | - |
dc.date.created | 2020-11-25 | - |
dc.date.created | 2020-11-25 | - |
dc.date.issued | 2020-10 | - |
dc.identifier.citation | 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020, pp.3928 - 3933 | - |
dc.identifier.issn | 1062-922X | - |
dc.identifier.uri | http://hdl.handle.net/10203/277787 | - |
dc.description.abstract | Detection and classification of road-objects like cars, pedestrians, and cyclists is the first step in autonomous driving. In particular, point-wise object segmentation for 3D point clouds is essential to estimate the precise appearances of the road-objects. In this paper, we propose SPriorSeg, a fast and accurate point-level object segmentation for point clouds by integrating the strengths of deep convolutional auto-encoder and region growing algorithm. Semantic segmentation using the light-weighted convolutional auto-encoder generates semantic prior by labeling a spherical projection image of point clouds pixel-by-pixel with classes of road-objects. The region growing algorithm achieves pixel-wise instance segmentation by taking into account semantic prior and geometric features between neighboring pixels. We build a well-balanced, pixel-level labeled dataset for all classes using 3D bounding boxes and point clouds from the KITTI object dataset. The dataset is employed to train our light-weighted neural network for semantic segmentation and demonstrate the performance of both semantic and instance segmentation of SPriorSeg. | - |
dc.language | English | - |
dc.publisher | IEEE | - |
dc.title | SPriorSeg: Fast Road-Object Segmentation using Deep Semantic Prior for Sparse 3D Point Clouds | - |
dc.type | Conference | - |
dc.identifier.wosid | 000687430603150 | - |
dc.identifier.scopusid | 2-s2.0-85098877057 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 3928 | - |
dc.citation.endingpage | 3933 | - |
dc.citation.publicationname | 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 | - |
dc.identifier.conferencecountry | CN | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1109/SMC42975.2020.9282832 | - |
dc.contributor.localauthor | Kim, Jong-Hwan | - |
dc.contributor.nonIdAuthor | Park, Byungjae | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.