1 code implementation • 24 Jan 2021 • Nannan Qin, Weikai Tan, Lingfei Ma, Dedong Zhang, Jonathan Li
Ground filtering has remained a widely studied but incompletely resolved bottleneck for decades in the automatic generation of high-precision digital elevation model, due to the dramatic changes of topography and the complex structures of objects.
no code implementations • 31 Dec 2020 • Lucas Prado Osco, Mauro dos Santos de Arruda, Diogo Nunes Gonçalves, Alexandre Dias, Juliana Batistoti, Mauricio de Souza, Felipe David Georges Gomes, Ana Paula Marques Ramos, Lúcio André de Castro Jorge, Veraldo Liesenberg, Jonathan Li, Lingfei Ma, José Marcato Junior, Wesley Nunes Gonçalves
In the corn plantation datasets (with both growth phases, young and mature), our approach returned a mean absolute error (MAE) of 6. 224 plants per image patch, a mean relative error (MRE) of 0. 1038, precision and recall values of 0. 856, and 0. 905, respectively, and an F-measure equal to 0. 876.
no code implementations • 21 Oct 2020 • Weikai Tan, Dedong Zhang, Lingfei Ma, Ying Li, Lanying Wang, Jonathan Li
Stack interchanges are essential components of transportation systems.
no code implementations • 20 May 2020 • Ying Li, Lingfei Ma, Zilong Zhong, Fei Liu, Dongpu Cao, Jonathan Li, Michael A. Chapman
In this paper, we provide a systematic review of existing compelling deep learning architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving such as segmentation, detection, and classification.
1 code implementation • 18 Mar 2020 • Weikai Tan, Nannan Qin, Lingfei Ma, Ying Li, Jing Du, Guorong Cai, Ke Yang, Jonathan Li
Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definition (HD) mapping.