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Wang, Xufei (author), Yang, Z. (author), Cheng, Xiaojun (author), Stoter, J.E. (author), Xu, Wenbing (author), Wu, Zhenlun (author), Nan, L. (author)
Registering point clouds of forest environments is an essential prerequisite for LiDAR applications in precision forestry. State-of-the-art methods for forest point cloud registration require the extraction of individual tree attributes, and they have an efficiency bottleneck when dealing with point clouds of real-world forests with dense...
journal article 2023
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Xu, Yabin (author), Nan, L. (author), Zhou, Laishui (author), Wang, Jun (author), Wang, C.C. (author)
Reconstruction of high-fidelity 3D objects or scenes is a fundamental research problem. Recent advances in RGB-D fusion have demonstrated the potential of producing 3D models from consumer-level RGB-D cameras. However, due to the discrete nature and limited resolution of their surface representations (e.g., point or voxel based), existing...
journal article 2022
document
Fan, Guangpeng (author), Xu, Zhenyu (author), Wang, Jinhu (author), Nan, L. (author), Xiao, Huijie (author), Xin, Zhiming (author), Chen, Feixiang (author)
Complexity of forest structure is an important factor contributing to uncertainty in aboveground biomass estimates. In this study, we present a new method for reducing uncertainty in forest aboveground biomass (AGB) estimation based on plot-level terrestrial laser scanner (TLS) point clouds reconstruction. The method estimates the total AGB...
journal article 2022