A Comparative Study of Semantic Segmentation Using Deep Neural Networks in a GNSS-Denied Underground Parking Lot

EasyChair Preprint 10125

Abstract

Deep neural networks (DNNs) in intelligent point cloud processing have achieved remarkable progress in recent years. Most existing methods and models were adopted on either outdoor or indoor scenes while very few previous studies were conducted in GNSS-denied environments. In this paper, we carried out a comparative study in semantic segmentation outputs using different DNNs in an underground parking lot dataset. Manually labeled indoor point cloud data were trained and tested using 7 different DNNs (e.g. PointNet, KPConv, FPConv, BAAF-Net, etc.). Our experiments demonstrated how well different DNNs perform in GNSS-denied environments with performance assessments in mIoU, Mean Accuracy (mAcc), Overall Accuracy (OA), as well as visualization outputs. The main contribution of this comparative study is to compare state-of-the-art DNN algorithms’ performance in semantic segmentation directly on the raw indoor mobile laser scanning (iMLS) data from a GNSS-denied underground parking lot and evaluate the effectiveness and potentials of different DNNs in underground 3D taskings. Draw upon that, which current algorithms are optimal and how future work in GNSS-denied environments can be inspired and implemented would be discussed.

Keyphrases: GNSS-denied environment, deep learning, point cloud processing, semantic segmentation

@booklet{EasyChair:10125,
  author    = {Kristie Hu and Jing Du and Xinghan Gong and Lingfei Ma and Jonathan Li},
  title     = {A Comparative Study of Semantic Segmentation Using Deep Neural Networks in a GNSS-Denied Underground Parking Lot},
  howpublished = {EasyChair Preprint 10125},
  year      = {EasyChair, 2023}}