mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2025-06-26 18:18:11 +00:00
542 lines
19 KiB
Plaintext
542 lines
19 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "b66179c8",
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"metadata": {},
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"source": [
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"# 1.预处理数据"
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]
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},
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{
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"cell_type": "markdown",
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"id": "533db061",
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"metadata": {},
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"source": [
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"## 1.1 对点云数据进行处理 - 滤除环视范围外过远的点"
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]
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},
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{
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"cell_type": "markdown",
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"id": "864843b2",
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"metadata": {},
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"source": [
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"1. 解析 images.txt，计算所有相机在世界坐标系下的光心（位姿）。 \n",
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"2. 使用 Open3D 加载 .pcd 点云，并基于与最近相机光心的距离进行滤除。 \n",
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"3. 输出相机光心的范围（坐标轴最小/最大值）以及被滤除的点云数量。 \n",
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"4. 将保留的点云按 COLMAP points3D.txt 格式保存，并额外生成一个 PLY 文件，其中红色标记被删除的点，原色保留未删除点。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5dc3ac56",
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"metadata": {},
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"source": [
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"#### ipynb内部函数 -- 未进行地面与boundingbox对齐"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cc2e8c18",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import open3d as o3d\n",
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"from scipy.spatial.transform import Rotation as R\n",
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"\n",
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"def filter_point_cloud(images_txt, pcd_path, output_points_txt, output_points_ply, output_ply_path):\n",
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" \"\"\"\n",
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" 根据相机位姿过滤点云，并保存结果。\n",
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"\n",
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" Args:\n",
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" images_txt (str): 包含相机位姿的 images.txt 文件路径。\n",
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" pcd_path (str): 点云文件路径。\n",
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" output_points_txt (str): 保存过滤后点云的 points3D.txt 文件路径。\n",
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" output_ply_path (str): 保存带颜色标记的 PLY 文件路径。\n",
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" \"\"\"\n",
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" def load_camera_centers(images_txt):\n",
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" centers = []\n",
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" with open(images_txt, 'r') as f:\n",
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" for line in f:\n",
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" if line.startswith('#') or len(line.strip()) == 0:\n",
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" continue\n",
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" elems = line.strip().split()\n",
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" if len(elems) < 10:\n",
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" continue\n",
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" qw, qx, qy, qz = map(float, elems[1:5])\n",
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" tx, ty, tz = map(float, elems[5:8])\n",
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" rot = R.from_quat([qx, qy, qz, qw]).as_matrix()\n",
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" center = -rot.T @ np.array([tx, ty, tz])\n",
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" centers.append(center)\n",
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" return np.array(centers)\n",
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"\n",
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" # 1. 加载相机中心\n",
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" camera_centers = load_camera_centers(images_txt)\n",
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"\n",
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" # 2. 计算平均中心和最大偏移\n",
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" center_mean = camera_centers.mean(axis=0)\n",
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" offsets = np.abs(camera_centers - center_mean)\n",
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" max_offset = offsets.max(axis=0)\n",
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"\n",
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" # 3. 加载点云\n",
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" pcd = o3d.io.read_point_cloud(pcd_path)\n",
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" points = np.asarray(pcd.points)\n",
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" colors = np.asarray(pcd.colors) if pcd.has_colors() else np.ones_like(points)\n",
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"\n",
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" # 4. 构建包围盒并裁剪点云\n",
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" min_bound = center_mean - max_offset - 15\n",
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" max_bound = center_mean + max_offset + 15\n",
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" aabb = o3d.geometry.AxisAlignedBoundingBox(min_bound, max_bound)\n",
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" pcd_cropped = pcd.crop(aabb)\n",
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"\n",
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" # 5. 统计信息\n",
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" total_points = len(points)\n",
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" kept_points = np.asarray(pcd_cropped.points)\n",
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" kept_colors = np.asarray(pcd_cropped.colors) if pcd_cropped.has_colors() else np.ones_like(kept_points)\n",
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" removed_mask = np.ones(total_points, dtype=bool)\n",
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" kept_indices = aabb.get_point_indices_within_bounding_box(pcd.points)\n",
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" removed_mask[kept_indices] = False\n",
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" removed_points = points[removed_mask]\n",
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" removed_colors = colors[removed_mask]\n",
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"\n",
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" print(\"相机中心范围：\")\n",
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" print(f\" X: [{min_bound[0]:.3f}, {max_bound[0]:.3f}]\")\n",
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" print(f\" Y: [{min_bound[1]:.3f}, {max_bound[1]:.3f}]\")\n",
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" print(f\" Z: [{min_bound[2]:.3f}, {max_bound[2]:.3f}]\")\n",
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" print(f\"总点数：{total_points}\")\n",
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" print(f\"保留点数：{len(kept_points)}\")\n",
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" print(f\"删除点数：{len(removed_points)}\")\n",
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"\n",
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" # 6. 保存 points3D.txt (速度太慢，直接使用 ply )\n",
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" with open(output_points_txt, \"w\") as f:\n",
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" f.write(\"# 3D point list with one line of data per point:\\n\")\n",
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" f.write(\"# POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[]\\n\")\n",
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" for idx, (pt, col) in enumerate(zip(kept_points, kept_colors), start=1):\n",
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" r, g, b = (col * 255).astype(int)\n",
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" f.write(f\"{idx} {pt[0]:.6f} {pt[1]:.6f} {pt[2]:.6f} {r} {g} {b} 0\\n\")\n",
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" # 保存为 ply 文件\n",
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" pcd_kept = o3d.geometry.PointCloud()\n",
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" pcd_kept.points = o3d.utility.Vector3dVector(kept_points)\n",
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" pcd_kept.colors = o3d.utility.Vector3dVector(kept_colors)\n",
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" o3d.io.write_point_cloud(output_points_ply, pcd_kept)\n",
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" print(\"保留的点云已保存为 {output_points_ply}\")\n",
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" \n",
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" # 7. 保存带颜色的 PLY 文件\n",
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" all_points = np.vstack((kept_points, removed_points))\n",
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" removed_colors_red = np.tile([1.0, 0.0, 0.0], (len(removed_points), 1))\n",
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" all_colors = np.vstack((kept_colors, removed_colors_red))\n",
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" pcd_all = o3d.geometry.PointCloud()\n",
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" pcd_all.points = o3d.utility.Vector3dVector(all_points)\n",
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" pcd_all.colors = o3d.utility.Vector3dVector(all_colors)\n",
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" o3d.io.write_point_cloud(output_ply_path, pcd_all)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "80b924be",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"相机中心范围：\n",
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" X: [-0.200, 28.270]\n",
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" Y: [-16.082, 1.699]\n",
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" Z: [-9.392, 0.714]\n",
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"总点数：16404321\n",
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"保留点数：10684495\n",
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"删除点数：5719826\n",
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"保留的点云已保存为 {output_points_ply}\n"
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]
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}
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],
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"source": [
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"import os\n",
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"folder_path = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results'\n",
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"\n",
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"images_txt = os.path.join(folder_path, 'sparse/0/images.txt')\n",
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"pcd_path = os.path.join(folder_path, 'pcd/all_raw_points.pcd')\n",
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"output_points_ply = os.path.join(folder_path, 'pcd/points3D.ply')\n",
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"vis_ply_path = os.path.join(folder_path, 'pcd/vis_filter.ply')\n",
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"output_points_txt = os.path.join(folder_path, 'pcd/points3D.txt')\n",
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"filter_point_cloud(images_txt,pcd_path,output_points_txt, output_points_ply,vis_ply_path)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1a4206e8",
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"metadata": {},
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"source": [
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"#### 调用 crop_points.py 文件"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8d8c55ed",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"总点数：16404321，保留：15254842，删除：1149479\n"
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]
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}
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],
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"source": [
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"!python crop_points.py \\\n",
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" --images_txt /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/sparse/0/images.txt \\\n",
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" --pcd_path /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/all_raw_points.pcd \\\n",
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" --output_points_txt /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/points3D.txt \\\n",
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" --output_ply /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/filtered_colored.ply \\\n",
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" --margin 10.0"
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]
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},
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{
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"cell_type": "markdown",
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"id": "651ed72f",
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"metadata": {},
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"source": [
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"## 1.2 利用体素化降采样"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "56f5cba7",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"raw points len : 15254842\n",
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"downsample points len : 201667\n",
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"降采样后的点云已保存到 /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/sparse/0/points3D_filter_0.2.ply，体素大小：0.2\n"
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]
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}
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],
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"source": [
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"import os\n",
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"from tools.points_utils import voxel_downsample_and_save\n",
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"\n",
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"voxel_size = 0.2\n",
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"folder_path = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results'\n",
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"input_ply_path = os.path.join(folder_path,'pcd/points3D.ply')\n",
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"output_ply_path = os.path.join(folder_path,f'pcd/points3D_{voxel_size}.ply')\n",
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"input_ply_path = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/sparse/0/points3D_filter.ply'\n",
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"output_ply_path = f'/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/sparse/0/points3D_filter_{voxel_size}.ply'\n",
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"\n",
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"voxel_downsample_and_save(voxel_size, input_ply_path, output_ply_path) # ply downsample to ply\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8544bb3e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# pcd 2 ply\n",
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"# 将原pcd点云，转换为ply点云\n",
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"from tools.points_utils import pcd_2_ply\n",
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"pcd_file = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/all_raw_points.pcd'\n",
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"ply_file = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/all_raw_points.ply'\n",
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"\n",
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"pcd_2_ply(pcd_file,ply_file)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "2bf16bb1",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
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"[Open3D INFO] WebRTC GUI backend enabled.\n",
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"[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n",
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"正在写入COLMAP TXT文件: /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/points3D_filter_white.txt\n",
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"Converted /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/sparse/0/points3D_filter.ply to Colmap TXT in: /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/points3D_filter_white.txt\n"
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]
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}
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],
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"source": [
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"# pcd 2 txt\n",
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"from tools.points_utils import pcd_2_colmap_txt\n",
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"pcd_file = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/sparse/0/points3D_filter.ply'\n",
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"txt_file = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/points3D_filter_white.txt'\n",
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"\n",
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"pcd_2_colmap_txt(pcd_file, txt_file, is_white=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6fd0b306",
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"metadata": {},
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"source": [
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"选择一个合适的 points3D.ply 文件复制到 sparse/0 下"
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]
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},
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{
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"cell_type": "markdown",
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"id": "22e87bd3",
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"metadata": {},
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"source": [
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"## 1.2 Resize for more images"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "91ce4cb8",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import cv2\n",
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"from tqdm import tqdm\n",
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"\n",
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"def resize_images(input_dir, output_dir, extensions):\n",
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" \"\"\"\n",
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" 读取输入文件夹中的所有图片，调整为1/2大小后保存到输出文件夹\n",
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" \n",
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" Args:\n",
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" input_dir: 输入图片文件夹路径\n",
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" output_dir: 输出图片文件夹路径\n",
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" extensions: 支持的图片扩展名列表\n",
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" \"\"\"\n",
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" # 确保输出目录存在\n",
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" os.makedirs(output_dir, exist_ok=True)\n",
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" \n",
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" # 获取所有图片文件\n",
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" image_files = []\n",
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" for file in os.listdir(input_dir):\n",
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" if any(file.lower().endswith(ext) for ext in extensions):\n",
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" image_files.append(file)\n",
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" \n",
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" if not image_files:\n",
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" print(f\"在 {input_dir} 中未找到支持的图片文件\")\n",
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" return\n",
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" \n",
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" print(f\"找到 {len(image_files)} 张图片\")\n",
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" \n",
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" # 处理每张图片\n",
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" count = 0\n",
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" for file in tqdm(image_files, desc=\"处理中\"):\n",
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" input_path = os.path.join(input_dir, file)\n",
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" output_path = os.path.join(output_dir, file)\n",
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" \n",
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" try:\n",
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" # 读取图片\n",
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" img = cv2.imread(input_path)\n",
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" if img is None:\n",
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" print(f\"警告: 无法读取图片 {input_path}，跳过\")\n",
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" continue\n",
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" \n",
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" # 获取原始尺寸\n",
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" height, width = img.shape[:2]\n",
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" \n",
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" # 计算新尺寸\n",
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" new_width = width // 2\n",
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" new_height = height // 2\n",
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" \n",
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" # 调整尺寸\n",
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" resized_img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_AREA)\n",
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" \n",
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" # 保存图片\n",
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" cv2.imwrite(output_path, resized_img)\n",
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" \n",
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" # 输出尺寸信息\n",
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" if count == 0:\n",
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" print(f\"{file}: {width}x{height} -> {new_width}x{new_height}\")\n",
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" count += 1\n",
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" except Exception as e:\n",
|
||
" print(f\"错误: 处理图片 {input_path} 时出错: {str(e)}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "e9b4da36",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"input_path = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01/depth_maps'\n",
|
||
"output_path = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01/depth_maps_2'\n",
|
||
"exts = ['jpg','jpeg','png']\n",
|
||
"\n",
|
||
"resize_images(input_path, output_path, exts)\n",
|
||
"print(\"所有图片处理完成！\") "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3830cb91",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 2. LIVO2和Colmap的重建对比实验"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7dd6ff70",
|
||
"metadata": {},
|
||
"source": [
|
||
"colmap无法恢复相机位姿；所以这里我们使用livo2恢复位姿后，用colmap 进行三角测量获取关键点\n",
|
||
"https://www.cnblogs.com/Todd-Qi/p/15080968.html"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4ad9f127",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 2.1 基于Livo2位姿进行稀疏重建"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "40e8c7f1",
|
||
"metadata": {},
|
||
"source": [
|
||
"colmap无法恢复相机位姿；所以这里我们使用livo2恢复位姿后，用colmap 进行三角测量获取关键点\n",
|
||
"https://www.cnblogs.com/Todd-Qi/p/15080968.html"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "497d715a",
|
||
"metadata": {},
|
||
"source": [
|
||
"1. 准备来自Livo2的位姿和相机数据 cameras.txt, images.txt\n",
|
||
" 将内参(camera intrinsics) 放入cameras.txt， 外参(camera extrinsics)放入 images.txt , points3D.txt 为空 \n",
|
||
" - images.txt 中全部 0.0 0.0 -1 删除; \n",
|
||
" - points3D.txt 内容清空;\n",
|
||
" - cameras.txt 中的内参进行修改 (对输入图像全部进行了 resize 操作，因此需要修改相机内参，将fx, fy, cx, cy 都除以2)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "497596e0",
|
||
"metadata": {},
|
||
"source": [
|
||
"2. 特征匹配与特征提取 \n",
|
||
"``` bash\n",
|
||
" colmap feature_extractor \\\n",
|
||
" --database_path /path/to/project/database.db \\ \n",
|
||
" --image_path /path/to/project/images\n",
|
||
"```\n",
|
||
"``` bash\n",
|
||
" colmap exhaustive_matcher \\\n",
|
||
" --database_path /path/to/project/database.db\n",
|
||
"```"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "590c5ba8",
|
||
"metadata": {},
|
||
"source": [
|
||
"3. 三角化重建 (保存的点云和其他文件均为bin格式)\n",
|
||
"``` bash\n",
|
||
" colmap point_triangulator \\\n",
|
||
" --database_path /path/to/project/database.db \\\n",
|
||
" --image_path /path/to/project/images \\\n",
|
||
" --input_path /path/to/sparse_model \\\n",
|
||
" --output_path /path/to/triangulated_model\n",
|
||
"\n",
|
||
"```\n",
|
||
"\n",
|
||
"查看txt结果\n",
|
||
"``` bash\n",
|
||
" colmap model_converter \\\n",
|
||
" --input_path 0 \\\n",
|
||
" --output_path 0_txt_from_livo2 \\\n",
|
||
" --output_type TXT\n",
|
||
"```"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6c2b853f",
|
||
"metadata": {},
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d359ac68",
|
||
"metadata": {},
|
||
"source": [
|
||
"4. 稠密重建(optional)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "77250b4b",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 3.训练"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "d44f71f7",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# baseline raw gs for training\n",
|
||
"!CUDA_VISIBLE_DEVICES=1 python train.py \\\n",
|
||
" -s data/tree_01_livo2 \\\n",
|
||
" -m data/tree_01_livo2/outputs/3dgs_baseline\n",
|
||
" \n",
|
||
"# render\n",
|
||
"!CUDA_VISIBLE_DEVICES=1 python render.py \\\n",
|
||
" -s data/tree_01_colmap \\\n",
|
||
" -m data/tree_01_colmap/outputs/3dgs_baseline "
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"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": "3.8.18"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|