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dc.contributor.authorZarzar, Jesus
dc.contributor.authorGiancola, Silvio
dc.contributor.authorGhanem, Bernard
dc.date.accessioned2019-12-23T06:08:33Z
dc.date.available2019-12-23T06:08:33Z
dc.date.issued2019-11-27
dc.identifier.urihttp://hdl.handle.net/10754/660739
dc.description.abstractIn autonomous driving pipelines, perception modules provide a visual understanding of the surrounding road scene. Among the perception tasks, vehicle detection is of paramount importance for a safe driving as it identifies the position of other agents sharing the road. In our work, we propose PointRGCN: a graph-based 3D object detection pipeline based on graph convolutional networks (GCNs) which operates exclusively on 3D LiDAR point clouds. To perform more accurate 3D object detection, we leverage a graph representation that performs proposal feature and context aggregation. We integrate residual GCNs in a two-stage 3D object detection pipeline, where 3D object proposals are refined using a novel graph representation. In particular, R-GCN is a residual GCN that classifies and regresses 3D proposals, and C-GCN is a contextual GCN that further refines proposals by sharing contextual information between multiple proposals. We integrate our refinement modules into a novel 3D detection pipeline, PointRGCN, and achieve state-of-the-art performance on the easy difficulty for the bird eye view detection task.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/1911.12236
dc.rightsArchived with thanks to arXiv
dc.titlePointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement
dc.typePreprint
dc.contributor.departmentKing Abdullah University of Science and Technology (KAUST), Saudi Arabia
dc.contributor.departmentVisual Computing Center (VCC)
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.eprint.versionPre-print
dc.identifier.arxivid1911.12236
kaust.personZarzar, Jesus
kaust.personGiancola, Silvio
kaust.personGhanem, Bernard
refterms.dateFOA2019-12-23T06:08:56Z


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