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from sklearn.decomposition import PCA | |
import plotly.graph_objects as go | |
import torch | |
def visualize_features_PCA( | |
xyz: torch.Tensor, | |
features: torch.Tensor, | |
): | |
"""visualize the features using PCA | |
Args: | |
xyz (torch.Tensor): [num_points, 3] | |
features (torch.Tensor): [num_points, feature_dim] | |
""" | |
xyz = xyz.cpu().numpy() | |
features = features.cpu().numpy() | |
pca = PCA(n_components=3) | |
pca_result = pca.fit_transform(features) # (N, channel) | |
# Normalize the results between 0 and 1 for color mapping | |
pca_result -= pca_result.min(axis=0) | |
pca_result /= pca_result.max(axis=0) | |
# Scale to 0-255 for RGB | |
pca_result = (pca_result * 255).astype(int) | |
# Convert to a format Plotly understands | |
color = ["rgb({},{},{})".format(r, g, b) for r, g, b in pca_result] | |
# Create interactive 3D scatter plot | |
fig = go.Figure( | |
data=[ | |
go.Scatter3d( | |
x=xyz[:, 0], | |
y=xyz[:, 1], | |
z=xyz[:, 2], | |
mode="markers", | |
marker=dict( | |
size=5, | |
color=color, # set color to an array/list of desired values | |
opacity=0.8, | |
), | |
) | |
] | |
) | |
fig.show() | |