JinyiXia's repositories
torch-points3d
Pytorch framework for doing deep learning on point clouds.
PointNet2
Processing pipeline designed to segment point clouds acquired in forests
pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
AdQSM
AdQSM is a new tree quantitative structure model (QSM) that can reconstruct the 3D branch geometry of individual tree from Terrestrial Laser Scanning (TLS) point clouds. Many attributes of the trunk or branch can also be quantitatively calculated (as shown in the table below). For example, tree volume (trunk and branch), DBH and tree height parameters can be extracted directly. It allows point clouds collected by different sensors to serve as input point clouds. In addition to TLS, UAV LiDAR, mobile LiDAR, SLAM and even photogrammetry are also included under the premise of appropriate point cloud density.
TreeQSM
Quantitative Structure Models of Single Trees from Laser Scanner Data
rGEDI
rGEDI: An R Package for NASA's Global Ecosystem Dynamics Investigation (GEDI) Data Visualization and Processing.
TreeLS
R functions for processing individual tree TLS point clouds
lidUrb
Urban trees analyses from terrestrial laser scanning
optqsm
Automatically optimise and parallelise TreeQSM
adtree
Accurate, Detailed, and Automatic Modelling of Laser-Scanned Trees
treegraph
Extract structural models from point clouds of individual trees.