Closed titlezi closed 1 year ago
Hi Tian,
Thank you for your interest in our project.
We generate mesh over-segmentation results only in step-1. You can simply run PSSNet_pipeline_for_GCN
in step-1 to generate all data for step-2.
In step-2, we do not do the mesh over-segmentation, but we perform the GCN-based semantic segmentation. The input for step-2 should be in the folder spg_input
. You can simply copy the data in spg_input/pcl/
to ../datasets/custom_set/data/
and the data in spg_input/graph_edges/
to ../datasets/custom_set/pssnet_graphs/
. The data in segments_pnp
is only for visualization. The mesh over-segmentation results for step-2 are encoded in the data in spg_input/pcl/
, and the graph is encoded in the data in spg_input/graph_edges/
. Int step-2, we just use the pssnet_partition.py
to convert from .ply to .h5 for later processing in pssnet_main.py
.
Kind regards, Gao
Hi Tian,
Thank you for your interest in our project. We generate mesh over-segmentation results only in step-1. You can simply run
PSSNet_pipeline_for_GCN
in step-1 to generate all data for step-2. In step-2, we do not do the mesh over-segmentation, but we perform the GCN-based semantic segmentation. The input for step-2 should be in the folderspg_input
. You can simply copy the data inspg_input/pcl/
to../datasets/custom_set/data/
and the data inspg_input/graph_edges/
to../datasets/custom_set/pssnet_graphs/
. The data insegments_pnp
is only for visualization. The mesh over-segmentation results for step-2 are encoded in the data inspg_input/pcl/
, and the graph is encoded in the data inspg_input/graph_edges/
. Int step-2, we just use thepssnet_partition.py
to convert from .ply to .h5 for later processing inpssnet_main.py
.Kind regards, Gao
Thanks for your reply. Best regards! Tian
Hi author: your work is really nice! In the step-2, I don't understand which data is "mesh over-segmentation results" , I try to use
segments_pnp
and it failed. Does "mesh over-segmentation results" mean thefeature_pnp
, and "graphs" meanspg_inputs/graphs_edges
?Yours! Best regards! Tian