--- title: PointNet-ov-fp16 canonical_url: "https://www.modelscope.cn/models/rainhenry/PointNet-ov-fp16" md_url: "https://www.modelscope.cn/models/rainhenry/PointNet-ov-fp16.md" repository: rainhenry/PointNet-ov-fp16 chinese_name: PointNet-ov-fp16 last_updated: 2025-12-09 library_name: - other frameworks: - other downloads: 0 stars: 0 --- # PointNet-ov-fp16 > PointNet-ov-fp16 - rainhenry 在 ModelScope 开源的模型。https://github.com/openvinotoolkit/openvinonotebooks/blob/latest/notebooks/3D-segmentation-point-clouds/3D-segmentation-point-clouds.ipynb - **Repository**: rainhenry/PointNet-ov-fp16 - **Downloads**: 0 - **Stars**: 0 - **Last updated**: 2025-12-09 Source: https://www.modelscope.cn/models/rainhenry/PointNet-ov-fp16 --- ### 本模型适用于OpenVINO ### 示例代码 ```python #!/usr/bin/python3 from pathlib import Path from typing import Union import numpy as np import matplotlib.pyplot as plt import openvino as ov def load_data(point_file: Union[str, Path]): """ Load the point cloud data and convert it to ndarray Parameters: point_file: string, path of .pts data Returns: point_set: point clound represented in np.array format """ point_set = np.loadtxt(point_file).astype(np.float32) # normailization point_set = point_set - np.expand_dims(np.mean(point_set, axis=0), 0) # center dist = np.max(np.sqrt(np.sum(point_set**2, axis=1)), 0) point_set = point_set / dist # scale return point_set def visualize(point_set: np.ndarray): """ Create a 3D view for data visualization Parameters: point_set: np.ndarray, the coordinate data in X Y Z format """ fig = plt.figure(dpi=192, figsize=(4, 4)) ax = fig.add_subplot(111, projection="3d") X = point_set[:, 0] Y = point_set[:, 2] Z = point_set[:, 1] # Scale the view of each axis to adapt to the coordinate data distribution max_range = np.array([X.max() - X.min(), Y.max() - Y.min(), Z.max() - Z.min()]).max() * 0.5 mid_x = (X.max() + X.min()) * 0.5 mid_y = (Y.max() + Y.min()) * 0.5 mid_z = (Z.max() + Z.min()) * 0.5 ax.set_xlim(mid_x - max_range, mid_x + max_range) ax.set_ylim(mid_y - max_range, mid_y + max_range) ax.set_zlim(mid_z - max_range, mid_z + max_range) plt.tick_params(labelsize=5) ax.set_xlabel("X", fontsize=10) ax.set_ylabel("Y", fontsize=10) ax.set_zlabel("Z", fontsize=10) return ax core = ov.Core() # Read model model = core.read_model(model="chair_100.xml") point_data_path = Path(".") / "chair.pts" points = load_data(str(point_data_path)) X = points[:, 0] Y = points[:, 2] Z = points[:, 1] ax = visualize(points) ax.scatter3D(X, Y, Z, s=5, cmap="jet", marker="o", label="chair") ax.set_title("3D Visualization") plt.legend(loc="upper right", fontsize=8) plt.show(block=False) # Parts of a chair classes = ["back", "seat", "leg", "arm"] # Preprocess the input data point = points.transpose(1, 0) point = np.expand_dims(point, axis=0) # Print info about model input and output shape print(f"input shape: {model.input(0).partial_shape}") print(f"output shape: {model.output(0).partial_shape}") ##device = "CPU" device = "GPU" # Inference compiled_model = core.compile_model(model=model, device_name=device) output_layer = compiled_model.output(0) result = compiled_model([point])[output_layer] # Find the label map for all points of chair with highest confidence pred = np.argmax(result[0], axis=1) ax = visualize(point) for i, name in enumerate([0, 1, 2, 3]): XCur = [] YCur = [] ZCur = [] for j, nameCur in enumerate(pred): if name == nameCur: XCur.append(X[j]) YCur.append(Y[j]) ZCur.append(Z[j]) XCur = np.array(XCur) YCur = np.array(YCur) ZCur = np.array(ZCur) # add current point of the part ax.scatter(XCur, YCur, ZCur, s=5, cmap="jet", marker="o", label=classes[i]) ax.set_title("3D Segmentation Visualization") plt.legend(loc="upper right", fontsize=8) plt.show() ``` ### 运行结果 ------------------ ``` /home/rainhenry/AIGC/20251208_3dpoint/model/./../infer2.py:71: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored ax.scatter3D(X, Y, Z, s=5, cmap="jet", marker="o", label="chair") input shape: [1,3,?] output shape: [1,?,4] /home/rainhenry/AIGC/20251208_3dpoint/model/./../infer2.py:113: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored ax.scatter(XCur, YCur, ZCur, s=5, cmap="jet", marker="o", label=classes[i]) ``` 然后会有两个图表： ![](image.jpg) ### 环境依赖参考 ``` Package Version ------------------ ----------- contourpy 1.3.3 cycler 0.12.1 filelock 3.19.1 fonttools 4.61.0 fsspec 2025.9.0 Jinja2 3.1.6 kiwisolver 1.4.9 MarkupSafe 2.1.5 matplotlib 3.10.7 mpmath 1.3.0 networkx 3.5 numpy 2.2.6 openvino 2025.4.0 openvino-telemetry 2025.2.0 packaging 25.0 pillow 12.0.0 pip 24.0 pyparsing 3.2.5 python-dateutil 2.9.0.post0 setuptools 70.2.0 six 1.17.0 sympy 1.14.0 torch 2.9.1+cpu tqdm 4.67.1 typing_extensions 4.15.0 ``` ### 其他参考 https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/3D-segmentation-point-clouds/3D-segmentation-point-clouds.ipynb