CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2023-07-24
Cited: 0
Clicked: 1228
Citations: Bibtex RefMan EndNote GB/T7714
Liwen LIU, Weidong YANG, Ben FEI. GeeNet: robust and fast point cloud completion for ground elevation estimation towards autonomous vehicles[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(7): 938-950.
@article{title="GeeNet: robust and fast point cloud completion for ground elevation estimation towards autonomous vehicles",
author="Liwen LIU, Weidong YANG, Ben FEI",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="25",
number="7",
pages="938-950",
year="2024",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300388"
}
%0 Journal Article
%T GeeNet: robust and fast point cloud completion for ground elevation estimation towards autonomous vehicles
%A Liwen LIU
%A Weidong YANG
%A Ben FEI
%J Frontiers of Information Technology & Electronic Engineering
%V 25
%N 7
%P 938-950
%@ 2095-9184
%D 2024
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300388
TY - JOUR
T1 - GeeNet: robust and fast point cloud completion for ground elevation estimation towards autonomous vehicles
A1 - Liwen LIU
A1 - Weidong YANG
A1 - Ben FEI
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 25
IS - 7
SP - 938
EP - 950
%@ 2095-9184
Y1 - 2024
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2300388
Abstract: ground elevation estimation is vital for numerous applications in autonomous vehicles and intelligent robotics including three-dimensional object detection, navigable space detection, point cloud matching for localization, and registration for mapping. However, most works regard the ground as a plane without height information, which causes inaccurate manipulation in these applications. In this work, we propose GeeNet, a novel end-to-end, lightweight method that completes the ground in nearly real time and simultaneously estimates the ground elevation in a grid-based representation. GeeNet leverages the mixing of two- and three-dimensional convolutions to preserve a lightweight architecture to regress ground elevation information for each cell of the grid. For the first time, GeeNet has fulfilled ground elevation estimation from semantic scene completion. We use the SemanticKITTI and SemanticPOSS datasets to validate the proposed GeeNet, demonstrating the qualitative and quantitative performances of GeeNet on ground elevation estimation and semantic scene completion of the point cloud. Moreover, the cross-dataset generalization capability of GeeNet is experimentally proven. GeeNet achieves state-of-the-art performance in terms of point cloud completion and ground elevation estimation, with a runtime of 0.88 ms.
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