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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%HTML\n",
"<style>\n",
" body {\n",
" --vscode-font-family: \"Roboto Thin\"\n",
" }\n",
"</style>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<center>\n",
"<h1> SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction \n",
" <br>and High-Quality Mesh Rendering</h1>\n",
"Antoine Guédon and Vincent Lepetit"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import torch\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import open3d as o3d\n",
"from pytorch3d.io import load_objs_as_meshes\n",
"from pytorch3d.renderer import (\n",
" AmbientLights,\n",
" RasterizationSettings, \n",
" MeshRenderer, \n",
" MeshRasterizer, \n",
" SoftPhongShader,\n",
" )\n",
"from pytorch3d.renderer.blending import BlendParams\n",
"from sugar_scene.gs_model import GaussianSplattingWrapper\n",
"from sugar_scene.sugar_model import SuGaR, load_refined_model\n",
"from sugar_utils.spherical_harmonics import SH2RGB"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"numGPU = 0\n",
"torch.cuda.set_device(numGPU)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load data and vanilla Gaussian Splatting"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ========== Loading parameters ==========\n",
"use_eval_split = False\n",
"n_skip_images_for_eval_split = 8\n",
"\n",
"iteration_to_load = 7000\n",
"# iteration_to_load = 30_000\n",
"\n",
"load_gt_images = False\n",
"use_custom_bbox = False"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Choose a data directory (the directory that contains the images subdirectory)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Example\n",
"source_path = './data/nerfstudio/qant03/'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Choose a corresponding vanilla Gaussian Splatting checkpoint directory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Example\n",
"gs_checkpoint_path = './gaussian_splatting/output/7bdae844-6/'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load data and 3DGS checkpoint."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ====================Load NeRF model and training data====================\n",
"\n",
"# Load Gaussian Splatting checkpoint \n",
"print(f\"\\nLoading config {gs_checkpoint_path}...\")\n",
"if use_eval_split:\n",
" print(\"Performing train/eval split...\")\n",
"nerfmodel = GaussianSplattingWrapper(\n",
" source_path=source_path,\n",
" output_path=gs_checkpoint_path,\n",
" iteration_to_load=iteration_to_load,\n",
" load_gt_images=load_gt_images,\n",
" eval_split=use_eval_split,\n",
" eval_split_interval=n_skip_images_for_eval_split,\n",
" )\n",
"\n",
"print(f'{len(nerfmodel.training_cameras)} training images detected.')\n",
"print(f'The model has been trained for {iteration_to_load} steps.')\n",
"print(len(nerfmodel.gaussians._xyz) / 1e6, \"M gaussians detected.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Render with a refined SuGaR model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Choose a corresponding refined SuGaR checkpoint directory (located in `refined/<your scene>`)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Example\n",
"refined_sugar_folder = \"./output/refined/qant03/sugarfine_3Dgs7000_densityestim02_sdfnorm02_level03_decim1000000_normalconsistency01_gaussperface1/\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Choose a refinement iteration to load."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"refined_iteration_to_load = 15_000"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the refined SuGaR checkpoint."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"refined_sugar_path = os.path.join(refined_sugar_folder, f\"{refined_iteration_to_load}.pt\")\n",
"print(f\"\\nLoading config {refined_sugar_path}...\")\n",
"\n",
"refined_sugar = load_refined_model(refined_sugar_path, nerfmodel)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Render an image."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"onsamerow = True\n",
"also_render_vanilla_3dgs = False\n",
"\n",
"# -----Camera to render-----\n",
"cameras_to_use = nerfmodel.training_cameras\n",
"# cameras_to_use = nerfmodel.test_cameras\n",
"\n",
"cam_idx = np.random.randint(0, len(cameras_to_use.gs_cameras))\n",
"# --------------------------\n",
"\n",
"refined_sugar.eval()\n",
"refined_sugar.adapt_to_cameras(cameras_to_use)\n",
"\n",
"print(f\"Rendering image with index {cam_idx}.\")\n",
"print(\"Image name:\", cameras_to_use.gs_cameras[cam_idx].image_name)\n",
"\n",
"verbose = False\n",
"normalize_img = False\n",
"\n",
"if load_gt_images:\n",
" gt_rgb = nerfmodel.get_gt_image(cam_idx)\n",
" i_sugar = 2\n",
"else:\n",
" i_sugar = 1\n",
"\n",
"with torch.no_grad():\n",
" if also_render_vanilla_3dgs:\n",
" gs_image = nerfmodel.render_image(\n",
" nerf_cameras=cameras_to_use,\n",
" camera_indices=cam_idx).clamp(min=0, max=1)\n",
" \n",
" sugar_image = refined_sugar.render_image_gaussian_rasterizer(\n",
" nerf_cameras=cameras_to_use, \n",
" camera_indices=cam_idx,\n",
" # bg_color=1. * torch.Tensor([1.0, 1.0, 1.0]).to(rc_fine.device),\n",
" sh_deg=nerfmodel.gaussians.active_sh_degree,\n",
" compute_color_in_rasterizer=True,\n",
" ).nan_to_num().clamp(min=0, max=1)\n",
"\n",
"# Change this to adjust the size of the plot\n",
"plot_ratio = 2. # 0.7, 1.5, 5\n",
"\n",
"if also_render_vanilla_3dgs:\n",
" plt.figure(figsize=(10 * plot_ratio, 10 * plot_ratio))\n",
" plt.axis(\"off\")\n",
" plt.title(\"Vanilla 3DGS render\")\n",
" plt.imshow(gs_image.cpu().numpy())\n",
" plt.show()\n",
"plt.figure(figsize=(10 * plot_ratio, 10 * plot_ratio))\n",
"plt.axis(\"off\")\n",
"plt.title(\"Refined SuGaR render\")\n",
"plt.imshow(sugar_image.cpu().numpy())\n",
"plt.show()\n",
"torch.cuda.empty_cache()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Render with a traditional color texture for SuGaR mesh"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select the path to the textured mesh (i.e. the obj file in `refined_mesh/<your scene>`).<br>\n",
"If None, the path to the mesh will be automatically computed from the checkpoint path."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"refined_mesh_path = None"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load mesh."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if refined_mesh_path is None:\n",
" post_processed = False\n",
"\n",
" if post_processed:\n",
" post_processed_str = '_postprocessed'\n",
" else:\n",
" post_processed_str = ''\n",
"\n",
" scene_name = refined_sugar_path.split('/')[-3]\n",
" refined_mesh_dir = './output/refined_mesh'\n",
" refined_mesh_path = os.path.join(\n",
" refined_mesh_dir, scene_name,\n",
" refined_sugar_path.split('/')[-2].split('.')[0] + '.obj'\n",
" )\n",
" \n",
"print(f\"Loading refined mesh from {refined_mesh_path}, this could take a minute...\")\n",
"textured_mesh = load_objs_as_meshes([refined_mesh_path]).to(nerfmodel.device)\n",
"print(f\"Loaded textured mesh with {len(textured_mesh.verts_list()[0])} vertices and {len(textured_mesh.faces_list()[0])} faces.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# -----Camera index to render-----\n",
"cam_idx = np.random.randint(0, len(cameras_to_use))\n",
"# --------------------------\n",
"\n",
"faces_per_pixel = 1\n",
"max_faces_per_bin = 50_000\n",
"\n",
"mesh_raster_settings = RasterizationSettings(\n",
" image_size=(refined_sugar.image_height, refined_sugar.image_width),\n",
" blur_radius=0.0, \n",
" faces_per_pixel=faces_per_pixel,\n",
" # max_faces_per_bin=max_faces_per_bin\n",
")\n",
"lights = AmbientLights(device=nerfmodel.device)\n",
"rasterizer = MeshRasterizer(\n",
" cameras=cameras_to_use.p3d_cameras[cam_idx], \n",
" raster_settings=mesh_raster_settings,\n",
" )\n",
"renderer = MeshRenderer(\n",
" rasterizer=rasterizer,\n",
" shader=SoftPhongShader(\n",
" device=refined_sugar.device, \n",
" cameras=cameras_to_use.p3d_cameras[cam_idx],\n",
" lights=lights,\n",
" # blend_params=BlendParams(background_color=(0.0, 0.0, 0.0)),\n",
" blend_params=BlendParams(background_color=(1.0, 1.0, 1.0)),\n",
" )\n",
")\n",
"\n",
"with torch.no_grad(): \n",
" print(\"Rendering image\", cam_idx)\n",
" print(\"Image ID:\", cameras_to_use.gs_cameras[cam_idx].image_name)\n",
" \n",
" p3d_cameras = cameras_to_use.p3d_cameras[cam_idx]\n",
" rgb_img = renderer(textured_mesh, cameras=p3d_cameras)[0, ..., :3]\n",
" \n",
"# Change this to adjust the size of the plot\n",
"plot_ratio = 2.\n",
"\n",
"plt.figure(figsize=(10 * plot_ratio, 10 * plot_ratio))\n",
"plt.axis(\"off\")\n",
"plt.title(\"Refined SuGaR mesh with a traditional color UV texture\")\n",
"plt.imshow(rgb_img.cpu().numpy())\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "sugar",
"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"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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