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Convert the point cloud "ply" to "npy"
Hi there,
Thanks for the great work. I've tried to inference my own point cloud based on your 3D match pretrained model and network. But I met a problem that the weight asking for the input to be shaped at Nx7 while the point shape is just Nx3.
Even I took a look at the evaluation data loader (match3d_eval_loader.py), still confusing. I just wonder that how did you convert the point cloud from ply to npy? Because when I check the 3D_Match_eval_npy, it's already converted to shape Nx7. And Is the surface_normal_len need for inference?
Thank you in advance, Tuan
Hi,
The other 4 elements are surface normal (nx, ny, nz) and curvature (scalar). They are computed using PCL library. In fact, these 4 elements has almost no effect to the performance. You may simply train your own model using Nx3. Thanks.
Best Regards Jiaxin Li
tuanho27 [email protected] 于2021年1月10日周日 下午4:37写道:
Hi there,
Thanks for the great work. I've tried to inference my own point cloud based on your 3D match pretrained model and network. But I met a problem that the weight asking for the input to be shaped at Nx7 while the point shape is just Nx3.
Even I took a look at the evaluation data loader (match3d_eval_loader.py), still confusing. I just wonder that how did you convert the point cloud from ply to npy? Because when I check the 3D_Match_eval_npy, it's already converted to shape Nx7. And Is the surface_normal_len need for inference?
Thank you in advance, Tuan
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-- 黎嘉信 Jiaxin LI PhD, National University of Singapore B.S., Tsinghua University Mobile: 65-9094 1909 / 86-15201519053 Email: [email protected] / [email protected] [email protected]
Hi, thanks for the reply.
So, It seems that I cannot directly use the provided pretrained model to test my point cloud. PCL is the great lib, but I may dig into it later. I think I will try to re-train the model without the 4 added elements if it does not very effective to the performance.
Regards, Tuan