TY - GEN
T1 - Deep Learning based 3D Object Detection in Indoor Environments
T2 - 6th CAA International Conference on Vehicular Control and Intelligence, CVCI 2022
AU - Jiang, Xiaohui
AU - Han, Lijin
AU - Liu, Hui
AU - Nie, Shida
AU - Wang, Shihao
AU - Wen, Yan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Recently, the performance of object detection models have been efficiently improved with the application of deep learning in point clouds. However, as far as we know, most proposed reviews focus on outdoor scenes for autonomous driving. So in this paper, we provide a comprehensive review of 3D object detection for point clouds in cluttered indoor environments, which is widely used in the fields of robotics and augmented reality. Firstly, we introduce three most frequently used indoor datasets. Then, we review the representative detection models in recent years and sort these methods into two classifications, segmentation-based models and non-segmentation models. The characteristics of each method are summarized and the results are compared on three different datasets. Lastly, we conclude the insightful observations and future works.
AB - Recently, the performance of object detection models have been efficiently improved with the application of deep learning in point clouds. However, as far as we know, most proposed reviews focus on outdoor scenes for autonomous driving. So in this paper, we provide a comprehensive review of 3D object detection for point clouds in cluttered indoor environments, which is widely used in the fields of robotics and augmented reality. Firstly, we introduce three most frequently used indoor datasets. Then, we review the representative detection models in recent years and sort these methods into two classifications, segmentation-based models and non-segmentation models. The characteristics of each method are summarized and the results are compared on three different datasets. Lastly, we conclude the insightful observations and future works.
KW - 3D object detection
KW - deep learning
KW - indoor environment
KW - point cloud
UR - https://www.scopus.com/pages/publications/85144613955
U2 - 10.1109/CVCI56766.2022.9964855
DO - 10.1109/CVCI56766.2022.9964855
M3 - Conference contribution
AN - SCOPUS:85144613955
T3 - 2022 6th CAA International Conference on Vehicular Control and Intelligence, CVCI 2022
BT - 2022 6th CAA International Conference on Vehicular Control and Intelligence, CVCI 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 October 2022 through 30 October 2022
ER -