{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from PIL import Image\n", "import torch\n", "import matplotlib.pyplot as plt\n", "from tqdm.auto import tqdm\n", "\n", "from point_e.models.download import load_checkpoint\n", "from point_e.models.configs import MODEL_CONFIGS, model_from_config\n", "from point_e.util.pc_to_mesh import marching_cubes_mesh\n", "from point_e.util.plotting import plot_point_cloud\n", "from point_e.util.point_cloud import PointCloud" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", "print('creating SDF model...')\n", "name = 'sdf'\n", "model = model_from_config(MODEL_CONFIGS[name], device)\n", "model.eval()\n", "\n", "print('loading SDF model...')\n", "model.load_state_dict(load_checkpoint(name, device))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Load a point cloud we want to convert into a mesh.\n", "pc = PointCloud.load('example_data/pc_corgi.npz')\n", "\n", "# Plot the point cloud as a sanity check.\n", "fig = plot_point_cloud(pc, grid_size=2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Produce a mesh (with vertex colors)\n", "mesh = marching_cubes_mesh(\n", " pc=pc,\n", " model=model,\n", " batch_size=4096,\n", " grid_size=32, # increase to 128 for resolution used in evals\n", " progress=True,\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Write the mesh to a PLY file to import into some other program.\n", "with open('mesh.ply', 'wb') as f:\n", " mesh.write_ply(f)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.18" }, "vscode": { "interpreter": { "hash": "b270b0f43bc427bcab7703c037711644cc480aac7c1cc8d2940cfaf0b447ee2e" } } }, "nbformat": 4, "nbformat_minor": 4 }