(video) [3D-DLAD-v4] Navya 3D Segmentation Dataset for large scale semantic segmentation, Alexandre Almin

3D-DLAD-v4 workshop : https://sites.google.com/view/3d-dlad-v4-iv2022/schedule

Abstract : Semantic segmentation for mapping applications in Autonomous Driving has gain a lot of attention lately, especially with the arrival of publicly available datasets such as SemanticKITTI, nuScenes and Waymo Open Dataset. These Autonomous driving datasets have progressively grown in size in the past few years to enable better deep representation learning. Active learning has re-gained attention recently to address reduction of annotation costs and dataset size. AL has remained relatively unexplored for AD datasets, especially on point cloud data from LiDARs. We conduct an applied study of Bayesian active learning applied on semantic segmentation task for dataset distillation, and the effect of data augmentation, “LiDAR dataset distillation within bayesian active learning framework – Understanding the effect of data augmentation”, recently published at VISAPP. In this context, the presentation will introduce a novel and fully annotated dataset called N3DS, and the complete production pipeline associated with. Our research results on dataset distillation applied to our newly N3DS dataset will also be presented.