How to generate bps files？ about ffhnet HOT 7 OPEN

For details how to generate basis point set, you can use the script from https://github.com/otaheri/bps_torch to do it. This basis point set are fixed for the entire dataset so you only need to generate once. @yys-abu

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For details how to generate basis point set, you can use the script from https://github.com/otaheri/bps_torch to do it. This basis point set are fixed for the entire dataset so you only need to generate once. @yys-abu

Thank you for your reply. Are you saying that I don't need to mark which point cloud can be captured and which cannot?
Just need to provide 1024 point clouds and the grasping posture relative to the center of the object

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For details how to generate basis point set, you can use the script from https://github.com/otaheri/bps_torch to do it. This basis point set are fixed for the entire dataset so you only need to generate once. @yys-abu

I found that the point cloud data size of the object is 1024x3, and the bps data of the object is 4096x1. How is this size of bps generated specifically? This is the code for generating bps:
import torch
import time
from bps_torch.bps import bps_torch
import open3d as o3d
import numpy as np
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
file_path = "kit_BakingVanilla_pcd000.pcd"

bps = bps_torch(bps_type='random_uniform',
n_bps_points=1024,
radius=1.,
n_dims=3,
custom_basis=None)

cloud1 = o3d.io.read_point_cloud(file_path)

points = cloud1.points

points_tensor = torch.tensor(points, dtype=torch.float32)

s = time.time()

bps_enc = bps.encode(points_tensor,
feature_type=['dists','deltas'],
x_features=None,
custom_basis=None)

print(time.time() - s)

deltas = bps_enc['deltas']
print(deltas)
bps_dec = bps.decode(deltas)

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For details how to generate basis point set, you can use the script from https://github.com/otaheri/bps_torch to do it. This basis point set are fixed for the entire dataset so you only need to generate once. @yys-abu

I used the following code to generate a basis point set. Do I need to generate 4096 dists and save them

import torch
import time
from bps_torch.bps import bps_torch
import open3d as o3d
import numpy as np
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
file_path = "kit_BakingVanilla_pcd000.pcd"
bps = bps_torch(bps_type='random_uniform',
n_bps_points=4096,
radius=1.,
n_dims=3,
custom_basis=None)
cloud1 = o3d.io.read_point_cloud(file_path)
points = cloud1.points
points_tensor = torch.tensor(points, dtype=torch.float32)
s = time.time()
bps_enc = bps.encode(points_tensor,
feature_type=['dists','deltas'],
x_features=None,
custom_basis=None)
print(time.time() - s)
dists= bps_enc['dists']
deltas = bps_enc['deltas']

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First, bps can encode arbitrary number of point cloud, either 1024 or more.
Second, you should only run
bps = bps_torch(bps_type='random_uniform', n_bps_points=1024, radius=1., n_dims=3, custom_basis=None) only once to generate a fixed basis point. Then you should save this generated bps to file. bps here is a numpy array.

When you use this basis point, you should load the saved basis point like this:

bps_path = os.path.join('path/to/basis_point_set.npy')  
bps_np = np.load(bps_path)  
bps = b_torch.bps_torch(custom_basis=bps_np)

So what you should save is the basis point set which will be needed for training and inference if you input bps encoded value to your neural network. Because during inference, you need exact same basis point set. You can also save the encoded bps and load it for training.

For details, please read the source code https://github.com/otaheri/bps_torch/blob/master/bps_torch/bps.py and also the paper https://arxiv.org/abs/1908.09186 because any small mistake will make training fails :D

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bps = bps_torch(bps_type='random_uniform', n_bps_points=1024, radius=1., n_dims=3, custom_basis=None)

bps = bps_torch(bps_type='random_uniform', n_bps_points=4096, radius=1., n_dims=3, custom_basis=None)

I use the above line of code to generate fixed basis point and I load fixed basis point:

bps_path = os.path.join('path/to/basis_point_set.npy')
bps = bps .encode(points, custom_basis=bps_np)
dists= bps_enc['dists']

There are 4096 pieces of data in the npy file of your dataset. Where did these 4096 pieces of data come from? Is it formed by encoding 4096 dists or generated 4096 fixed basis points？

This is your dataset:

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You first have bps (basis point set) which you can find in my dataset basis_point_set.npy.
The image you showed are already encoded 4096 dists based on basis_point_set.npy
My wechat: Albert_fq @yys-abu

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