\u5199\u771f\u2192\u5b9f\u5bf8\u70b9\u7fa4\uff1a\u7406\u8ad6\u3068\u5b8c\u5168\u81ea\u52d5\u5316\u30d1\u30a4\u30d7\u30e9\u30a4\u30f3<\/title>\n\n<style>\nbody{font-family:\"Meiryo\",sans-serif;max-width:1100px;margin:3em auto;line-height:1.8;background:#f8fafc;color:#222}\nh1,h2,h3{color:#153766;margin:2em 0 0.7em}\nh1{border-bottom:3px solid #153766;padding-bottom:0.3em}\nh2{border-left:6px solid #153766;padding-left:0.5em}\npre{background:#f4f4f4;border:1px solid #ccc;border-radius:8px;padding:1.1em;overflow-x:auto;font-size:0.92em}\ntable{border-collapse:collapse;margin:1em 0;width:100%}\nth,td{border:1px solid #ccc;padding:0.45em;text-align:left;font-size:0.95em}\n.important{background:#fbe7e8;border-left:6px solid #c62828;padding:1em;margin:1.2em 0}\n.tips{background:#e3f5ff;border-left:6px solid #039be5;padding:1em;margin:1.2em 0}\n.code-title{color:#2196f3;font-weight:bold;margin:1.6em 0 0.5em}\n<\/style>\n<\/head>\n<body>\n\n\n\n\n<h2>1. SfM \u306e\u6982\u8981\u3068\u51fa\u529b\u306e\u6027\u8cea<\/h2>\n<p>\n<b>\u672c\u6587\u7ae0\u306f\u57f7\u7b46\u9014\u4e2d\u306e\u3082\u306e\u3067\u3042\u308a\u3001\u30b3\u30fc\u30c9\u985e\u306e\u5b8c\u5168\u4fdd\u8a3c\u306f\u3067\u304d\u307e\u305b\u3093\u3002\u53c2\u8003\u7a0b\u5ea6\u306b\u304a\u8aad\u307f\u304f\u3060\u3055\u3044\u3002<br>SfM\uff08Structure from Motion\uff09<\/b> \u306f\u8907\u6570\u679a\u306e\u5199\u771f\u304b\u3089\u7279\u5fb4\u70b9\u3092\u62bd\u51fa\u3057\u3001<b>\u30ab\u30e1\u30e9\u4f4d\u7f6e\u30fb\u59ff\u52e2\u63a8\u5b9a + \u4e09\u89d2\u6e2c\u91cf<\/b>\u3067 3D \u70b9\u7fa4\u3092\u5fa9\u5143\u3059\u308b\u624b\u6cd5\u3067\u3059\u3002 \n\u4e3b\u306a OSS \/ \u30c4\u30fc\u30eb\uff1a<b>OpenMVG, COLMAP, OpenSfM, Regard3D<\/b> \u306a\u3069\u3002\n<\/p>\n<ul>\n<li>\u51fa\u529b\u3055\u308c\u308b\u70b9\u7fa4\u306f <b>\u76f8\u5bfe\u5f62\u72b6\u306e\u307f<\/b> \u3092\u4fdd\u6301\u3057\u3001\u7d76\u5bfe\u30b9\u30b1\u30fc\u30eb\uff08\u5b9f\u5bf8\uff09\u306f\u4e0d\u660e<\/li>\n<li>\u73fe\u5b9f\u306e\u300c1 m\u300d\u304c <code>1.0<\/code> \u3068\u306f\u9650\u3089\u305a\u3001<b>\u4efb\u610f\u30b9\u30b1\u30fc\u30eb<\/b> \u3067\u51fa\u529b\u3055\u308c\u308b<\/li>\n<\/ul>\n\n\n<h2>2. \u30b9\u30b1\u30fc\u30eb\uff08\u5b9f\u5bf8\uff09\u4ed8\u4e0e\u306e\u57fa\u672c\u30ed\u30b8\u30c3\u30af<\/h2>\n\n<h3>2.1 1 \u30da\u30a2\u306b\u3088\u308b\u30b9\u30b1\u30fc\u30eb\u5fa9\u5143<\/h3>\n<ol>\n<li>\u70b9\u7fa4\u5185\u3067<b>\u73fe\u5b9f\u306e\u8ddd\u96e2\u304c\u5206\u304b\u308b 2 \u70b9<\/b>\uff08A,B\uff09\u3092\u9078\u3076<\/li>\n<li>\u70b9\u7fa4\u4e0a\u306e\u8ddd\u96e2 <code>d_sfm = \u2016A \u2212 B\u2016<\/code> \u3092\u8a08\u7b97<\/li>\n<li>\u73fe\u5b9f\u8ddd\u96e2 <code>d_real<\/code> \u3092\u6e2c\u5b9a<\/li>\n<li><code>scale = d_real \/ d_sfm<\/code> \u3092\u7b97\u51fa<\/li>\n<li>\u70b9\u7fa4\u5168\u9802\u70b9\u306b <code>scale<\/code> \u500d\u3092\u639b\u3051\u308b \u2192 <b>\u5b9f\u5bf8\u70b9\u7fa4<\/b> \u5b8c\u6210<\/li>\n<\/ol>\n\n<h3>2.2 \u6d41\u308c\u306e\u6982\u5ff5\u56f3<\/h3>\n<pre><code> \u250c\u2500\u2500 \u5199\u771f\u7fa4 \u2500\u2500\u25b6 SfM \u2500\u25b6 \u76f8\u5bfe\u70b9\u7fa4\n \u2502\n \u2502\uff08\u57fa\u6e96\u30de\u30fc\u30ab\u30fc\u306e\u5b9f\u8ddd\u96e2 d_real\uff09\n \u2502\n \u25bc\n\u57fa\u6e96\u70b9 A,B \u306e\u5ea7\u6a19\u53d6\u5f97 \u2500\u2500\u25b6 \u8ddd\u96e2 d_sfm \u8a08\u7b97\n \u2502\n \u25bc\n scale = d_real \/ d_sfm\n \u2502\n \u25bc\n\u70b9\u7fa4\u5168\u4f53\u3092 scale \u500d \u2500\u2500\u25b6 \u5b9f\u5bf8\u70b9\u7fa4\uff08scaled.ply\uff09\n<\/code><\/pre>\n\n\n<h2>3. \u306a\u305c\u5b9f\u5bf8\u304c\u5fc5\u8981\u304b\uff1f<\/h2>\n<ul>\n<li><b>BIM\/CAD \u9023\u643a\uff1a<\/b> \u67f1\u9593\u8ddd\u96e2\u30fb\u58c1\u539a\u306a\u3069 mm\uff5ecm \u7cbe\u5ea6\u3067\u5fc5\u8981<\/li>\n<li><b>\u65bd\u5de5\u30fb\u6570\u91cf\u8a08\u7b97\uff1a<\/b> \u4f53\u7a4d\u30fb\u9762\u7a4d\u304c\u72c2\u3046\u3068\u30b3\u30b9\u30c8\u7b97\u51fa\u304c\u7834\u7dbb<\/li>\n<li><b>\u30a4\u30f3\u30d5\u30e9\u70b9\u691c\uff1a<\/b> \u4e80\u88c2\u5e45\u30fb\u6c88\u4e0b\u91cf\u3092\u5b9f\u9577\u3067\u8ffd\u8de1<\/li>\n<li><b>\u30ea\u30d0\u30fc\u30b9\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\uff1a<\/b> \u90e8\u54c1\u5bf8\u6cd5\u305d\u306e\u3082\u306e\u304c\u6210\u679c\u7269<\/li>\n<\/ul>\n<div class=\"important\">\u30b9\u30b1\u30fc\u30eb\u672a\u4ed8\u4e0e\u70b9\u7fa4\u306f \u201c\u9451\u8cde\u7269\u201d\u3002<b>\u9053\u5177\u306b\u3059\u308b\u306a\u3089\u5fc5\u305a\u5b9f\u5bf8\u5316<\/b>\u3002<\/div>\n\n\n<h2>4. \u30b9\u30b1\u30fc\u30eb\u3092\u5931\u3046\u30e1\u30ab\u30cb\u30ba\u30e0<\/h2>\n<p>\u5199\u771f\u30c7\u30fc\u30bf\u3060\u3051\u3067\u306f\u30ab\u30e1\u30e9\u2010\u88ab\u5199\u4f53\u9593\u306e\u672c\u5f53\u306e\u8ddd\u96e2\u304c\u5206\u304b\u3089\u305a\u3001 \n\u305f\u3068\u3048\u7126\u70b9\u8ddd\u96e2\u30fb\u30bb\u30f3\u30b5\u30b5\u30a4\u30ba\u304c\u5206\u304b\u3063\u3066\u3082<b>\u201c\u500d\u7387\u201d \u306f\u4e00\u610f\u306b\u6c7a\u307e\u3089\u306a\u3044<\/b>\u305f\u3081\u3067\u3059\u3002<\/p>\n\n<div class=\"tips\"><b>\u89e3\u6c7a\u7b56<\/b>\uff1a\u64ae\u5f71\u6642\u306b\u57fa\u6e96\u30de\u30fc\u30ab\u30fc\u3092\u5199\u3057\u3001\u5f8c\u51e6\u7406\u3067\u30b9\u30b1\u30fc\u30eb\u500d\u7387\u3092\u639b\u3051\u308b\u3002<\/div>\n\n\n<h2>5. \u30b9\u30b1\u30fc\u30eb\u4ed8\u4e0e\u3060\u3051\u3092\u884c\u3046\u6700\u5c0f Python \u30b5\u30f3\u30d7\u30eb<\/h2>\n\n<div class=\"code-title\">scale_pointcloud.py<\/div>\n<pre><code>import numpy as np\n\ndef load_ply_points(ply_path):\n with open(ply_path) as f:\n lines = f.readlines()\n end = [i for i,l in enumerate(lines) if l.strip()==\"end_header\"][0]\n pts = [list(map(float,l.split()[:3])) for l in lines[end+1:] if l.strip()]\n return np.array(pts)\n\ndef save_ply_points(pts, out_path):\n with open(out_path,\"w\") as f:\n f.write(\"ply\\nformat ascii 1.0\\n\")\n f.write(f\"element vertex {len(pts)}\\n\")\n f.write(\"property float x\\nproperty float y\\nproperty float z\\nend_header\\n\")\n for p in pts: f.write(f\"{p[0]} {p[1]} {p[2]}\\n\")\n\ndef scale_pointcloud(ply_in, ply_out, idxA, idxB, real_d):\n pts = load_ply_points(ply_in)\n scale = real_d \/ np.linalg.norm(pts[idxA] - pts[idxB])\n save_ply_points(pts*scale, ply_out)\n print(f\"\u2192 wrote {ply_out} (scale={scale:.6f})\")\n\n# \u4f8b: scale_pointcloud(\"raw.ply\",\"scaled.ply\",10,20,1.50)\n<\/code><\/pre>\n\n\n<h2>6. \u5b8c\u5168\u81ea\u52d5\u5316\u30d1\u30a4\u30d7\u30e9\u30a4\u30f3\u69cb\u6210<\/h2>\n\n<table>\n<thead><tr><th>\u6bb5\u968e<\/th><th>\u30c4\u30fc\u30eb<\/th><th>\u5b9f\u88c5<\/th><th>\u5099\u8003<\/th><\/tr><\/thead>\n<tbody>\n<tr><td>\u7279\u5fb4\u62bd\u51fa\u301c\u5bc6\u70b9\u7fa4<\/td><td>COLMAP CLI + CUDA<\/td><td>Docker\/Bash<\/td><td>GPU \u30d8\u30c3\u30c9\u30ec\u30b9<\/td><\/tr>\n<tr><td>\u30b9\u30b1\u30fc\u30eb\u4ed8\u4e0e<\/td><td>Python + Open3D<\/td><td>\u95a2\u6570\u5316<\/td><td>\u524d\u7ae0\u30ed\u30b8\u30c3\u30af<\/td><\/tr>\n<tr><td>\u5236\u5fa1\u30fb\u76e3\u8996<\/td><td>Python<\/td><td>CLI<\/td><td>\u30ea\u30c8\u30e9\u30a4 & \u30ed\u30b0<\/td><\/tr>\n<\/tbody>\n<\/table>\n\n\n<h2>7. Docker \u30a4\u30e1\u30fc\u30b8<\/h2>\n\n<div class=\"code-title\">docker\/Dockerfile<\/div>\n<pre><code>FROM nvidia\/cuda:12.3.0-runtime-ubuntu22.04\nRUN apt-get update && apt-get install -y colmap python3 python3-pip \\\n && pip install open3d numpy tqdm\nWORKDIR \/workspace\nENTRYPOINT [\"\/bin\/bash\"]\n<\/code><\/pre>\n\n\n<h2>8. \u5199\u771f\u2192\u5b9f\u5bf8\u70b9\u7fa4 \u30ef\u30f3\u30b3\u30de\u30f3\u30c9\u30b9\u30af\u30ea\u30d7\u30c8<\/h2>\n\n<div class=\"code-title\">run_sfm.py<\/div>\n<pre><code>#!\/usr\/bin\/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nUsage: python run_sfm.py <image_dir> <marker_distance_m>\n\u2192 scaled.ply \u3092\u51fa\u529b\n\"\"\"\nimport subprocess, tempfile, shutil, sys, time\nfrom pathlib import Path\nimport numpy as np, open3d as o3d\n\nIMGDIR, REAL = Path(sys.argv[1]), float(sys.argv[2])\nPT = (0,1); RETRY = 3\ndef sh(cmd,c=None): print(\">>\",\" \".join(map(str,cmd))); subprocess.check_call(cmd,cwd=c)\n\ndef colmap(img,work):\n db=work\/\"db.db\"; sp=work\/\"sparse\"; de=work\/\"dense\"\n sh([\"colmap\",\"feature_extractor\",\"--database_path\",db,\"--image_path\",img,\"--SiftExtraction.gpu_index\",\"0\"])\n sh([\"colmap\",\"exhaustive_matcher\",\"--database_path\",db,\"--SiftMatching.gpu_index\",\"0\"])\n sp.mkdir(); sh([\"colmap\",\"mapper\",\"--database_path\",db,\"--image_path\",img,\"--output_path\",sp])\n model=next(sp.iterdir())\n sh([\"colmap\",\"image_undistorter\",\"--image_path\",img,\"--input_path\",model,\"--output_path\",de,\"--output_type\",\"COLMAP\"])\n sh([\"colmap\",\"patch_match_stereo\",\"--workspace_path\",de,\"--workspace_format\",\"COLMAP\"])\n sh([\"colmap\",\"stereo_fusion\",\"--workspace_path\",de,\"--workspace_format\",\"COLMAP\",\n \"--input_type\",\"geometric\",\"--output_path\",de\/\"fused.ply\"])\n return de\/\"fused.ply\"\n\ndef scale(ply_in,ply_out,idxA,idxB,real):\n pc=o3d.io.read_point_cloud(str(ply_in)); pts=np.asarray(pc.points)\n s=real\/np.linalg.norm(pts[idxA]-pts[idxB]); pc.scale(s,center=(0,0,0))\n o3d.io.write_point_cloud(str(ply_out),pc); print(f\"[OK] {ply_out} scale={s:.6f}\")\n\ndef main():\n work=Path(tempfile.mkdtemp(prefix=\"sfm_\")); print(\"TMP:\",work)\n for i in range(1,RETRY+1):\n try: fused=colmap(IMGDIR,work); break\n except subprocess.CalledProcessError: print(\"[WARN] retry\",i); time.sleep(5)\n scale(fused,Path(\"scaled.ply\"),PT[0],PT[1],REAL); shutil.rmtree(work)\n\nif __name__==\"__main__\":\n if len(sys.argv)<3: sys.exit(\"Usage: run_sfm.py <img_dir> <dist_m>\")\n main()\n<\/code><\/pre>\n\n\n<h2>9. \u5b9f\u884c\u4f8b\uff08Docker \u30b3\u30f3\u30c6\u30ca\uff09<\/h2>\n<pre><code>docker run --gpus all -it \\\n -v \/abs\/path\/to\/images:\/imgs \\\n -v $(pwd):\/out colmap-auto \\\n python3 \/workspace\/run_sfm.py \/imgs 1.50\n# \u2192 \/out\/scaled.ply \u304c\u751f\u6210\n<\/code><\/pre>\n\n\n<h2>10. \u62e1\u5f35\u30a2\u30a4\u30c7\u30a2 & \u30dc\u30c8\u30eb\u30cd\u30c3\u30af\u5bfe\u7b56<\/h2>\n<ul>\n<li>\u30d5\u30a9\u30eb\u30c0\u76e3\u8996\uff1a<code>watchdog<\/code> \u3067\u81ea\u52d5\u6295\u5165\uff0fSlack \u901a\u77e5<\/li>\n<li>\u30de\u30fc\u30ab\u30fc\u81ea\u52d5\u691c\u51fa\uff1a\u8272\u95be\u5024 or AprilTag \u2192 <code>PT<\/code> \u81ea\u52d5\u6c7a\u5b9a<\/li>\n<li>SIFT \u62bd\u51fa\u306f <code>--SiftExtraction.num_threads=$(nproc)<\/code><\/li>\n<li>\u5927\u898f\u6a21\u6848\u4ef6\u306f <code>vocab_tree_matcher<\/code> \u3067\u30de\u30c3\u30c1\u30f3\u30b0\u9ad8\u901f\u5316<\/li>\n<li>PatchMatch \u753b\u50cf\u30b5\u30a4\u30ba\u3092 GPU VRAM \u306b\u5408\u308f\u305b\u3066 3k\uff5e4k<\/li>\n<li>Docker \u5171\u6709\u30e1\u30e2\u30ea\u4e0d\u8db3\u306f <code>--shm-size 8G<\/code><\/li>\n<\/ul>\n\n\n<h2>11. \u307e\u3068\u3081<\/h2>\n<ul>\n<li>\u7d76\u5bfe\u30b9\u30b1\u30fc\u30eb\u306f SfM \u51e6\u7406\u3067\u5fc5\u305a\u5931\u308f\u308c\u308b\u2014<b>\u57fa\u6e96\u30de\u30fc\u30ab\u30fc<\/b> \u3068 <b>\u4e00\u62ec\u500d\u7387<\/b> \u3067\u89e3\u6c7a<\/li>\n<li>Python \u6570\u884c\u3067\u30b9\u30b1\u30fc\u30eb\u4ed8\u4e0e\u3001Docker\uff0bCOLMAP \u3067\u5b8c\u5168\u81ea\u52d5\u5316<\/li>\n<li>API \u5316\u30fb\u5e38\u99d0\u30b5\u30fc\u30d3\u30b9\u5316\u3067 \u201c\u73fe\u5834\u304b\u3089\u5199\u771f\u3092\u6295\u3052\u308b\u3060\u3051\u201d \u904b\u7528\u3078<\/li>\n<\/ul>\n\n<div class=\"important\"><b>\u5199\u771f \u2192 \u5b9f\u5bf8\u70b9\u7fa4 \u306e\u81ea\u52d5\u5316\u306f\u300c\u3084\u308b\u304b\u30fb\u3084\u3089\u306a\u3044\u304b\u300d\u3060\u3051\u3002<\/b><\/div>\n\n<hr>\n<small>(C) 2025 Bee-Knowledge Design \u2014 No-nonsense, copy-ready.<\/small>\n\n<\/body>\n<\/html>\n\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u5199\u771f\u2192\u5b9f\u5bf8\u70b9\u7fa4\uff1a\u7406\u8ad6\u3068\u5b8c\u5168\u81ea\u52d5\u5316\u30d1\u30a4\u30d7\u30e9\u30a4\u30f3 1. SfM \u306e\u6982\u8981\u3068\u51fa\u529b\u306e\u6027\u8cea \u672c\u6587\u7ae0\u306f\u57f7\u7b46\u9014\u4e2d\u306e\u3082\u306e\u3067\u3042\u308a\u3001\u30b3\u30fc\u30c9\u985e\u306e\u5b8c\u5168\u4fdd\u8a3c\u306f\u3067\u304d\u307e\u305b\u3093\u3002\u53c2\u8003\u7a0b\u5ea6\u306b\u304a\u8aad\u307f\u304f\u3060\u3055\u3044\u3002SfM\uff08Structure from Motion\uff09 […]<\/p>\n","protected":false},"author":1,"featured_media":1589,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"saved_in_kubio":false,"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"vkexunit_cta_each_option":"","footnotes":""},"categories":[6,60],"tags":[73,27,51,44,36,72],"class_list":["post-1581","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-programing","category-60","tag-73","tag-27","tag-51","tag-44","tag-36","tag-72"],"aioseo_notices":[],"jetpack_featured_media_url":"https:\/\/beeknowledge.co.jp\/wp-content\/uploads\/2025\/08\/00pxtcj6pxtcj.jpg","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts\/1581","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1581"}],"version-history":[{"count":8,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts\/1581\/revisions"}],"predecessor-version":[{"id":1590,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts\/1581\/revisions\/1590"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/media\/1589"}],"wp:attachment":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1581"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1581"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1581"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

$\"\"$ <\/div>\n\n\n\n\n\u5199\u771f\u2192\u5b9f\u5bf8\u70b9\u7fa4\uff1a\u7406\u8ad6\u3068\u5b8c\u5168\u81ea\u52d5\u5316\u30d1\u30a4\u30d7\u30e9\u30a4\u30f3<\/title>\n\n<style>\nbody{font-family:\"Meiryo\",sans-serif;max-width:1100px;margin:3em auto;line-height:1.8;background:#f8fafc;color:#222}\nh1,h2,h3{color:#153766;margin:2em 0 0.7em}\nh1{border-bottom:3px solid #153766;padding-bottom:0.3em}\nh2{border-left:6px solid #153766;padding-left:0.5em}\npre{background:#f4f4f4;border:1px solid #ccc;border-radius:8px;padding:1.1em;overflow-x:auto;font-size:0.92em}\ntable{border-collapse:collapse;margin:1em 0;width:100%}\nth,td{border:1px solid #ccc;padding:0.45em;text-align:left;font-size:0.95em}\n.important{background:#fbe7e8;border-left:6px solid #c62828;padding:1em;margin:1.2em 0}\n.tips{background:#e3f5ff;border-left:6px solid #039be5;padding:1em;margin:1.2em 0}\n.code-title{color:#2196f3;font-weight:bold;margin:1.6em 0 0.5em}\n<\/style>\n<\/head>\n<body>\n\n\n\n\n<h2>1. SfM \u306e\u6982\u8981\u3068\u51fa\u529b\u306e\u6027\u8cea<\/h2>\n<p>\n<b>\u672c\u6587\u7ae0\u306f\u57f7\u7b46\u9014\u4e2d\u306e\u3082\u306e\u3067\u3042\u308a\u3001\u30b3\u30fc\u30c9\u985e\u306e\u5b8c\u5168\u4fdd\u8a3c\u306f\u3067\u304d\u307e\u305b\u3093\u3002\u53c2\u8003\u7a0b\u5ea6\u306b\u304a\u8aad\u307f\u304f\u3060\u3055\u3044\u3002<br>SfM\uff08Structure from Motion\uff09<\/b> \u306f\u8907\u6570\u679a\u306e\u5199\u771f\u304b\u3089\u7279\u5fb4\u70b9\u3092\u62bd\u51fa\u3057\u3001<b>\u30ab\u30e1\u30e9\u4f4d\u7f6e\u30fb\u59ff\u52e2\u63a8\u5b9a + \u4e09\u89d2\u6e2c\u91cf<\/b>\u3067 3D \u70b9\u7fa4\u3092\u5fa9\u5143\u3059\u308b\u624b\u6cd5\u3067\u3059\u3002 \n\u4e3b\u306a OSS \/ \u30c4\u30fc\u30eb\uff1a<b>OpenMVG, COLMAP, OpenSfM, Regard3D<\/b> \u306a\u3069\u3002\n<\/p>\n<ul>\n<li>\u51fa\u529b\u3055\u308c\u308b\u70b9\u7fa4\u306f <b>\u76f8\u5bfe\u5f62\u72b6\u306e\u307f<\/b> \u3092\u4fdd\u6301\u3057\u3001\u7d76\u5bfe\u30b9\u30b1\u30fc\u30eb\uff08\u5b9f\u5bf8\uff09\u306f\u4e0d\u660e<\/li>\n<li>\u73fe\u5b9f\u306e\u300c1 m\u300d\u304c <code>1.0<\/code> \u3068\u306f\u9650\u3089\u305a\u3001<b>\u4efb\u610f\u30b9\u30b1\u30fc\u30eb<\/b> \u3067\u51fa\u529b\u3055\u308c\u308b<\/li>\n<\/ul>\n\n\n<h2>2. \u30b9\u30b1\u30fc\u30eb\uff08\u5b9f\u5bf8\uff09\u4ed8\u4e0e\u306e\u57fa\u672c\u30ed\u30b8\u30c3\u30af<\/h2>\n\n<h3>2.1 1 \u30da\u30a2\u306b\u3088\u308b\u30b9\u30b1\u30fc\u30eb\u5fa9\u5143<\/h3>\n<ol>\n<li>\u70b9\u7fa4\u5185\u3067<b>\u73fe\u5b9f\u306e\u8ddd\u96e2\u304c\u5206\u304b\u308b 2 \u70b9<\/b>\uff08A,B\uff09\u3092\u9078\u3076<\/li>\n<li>\u70b9\u7fa4\u4e0a\u306e\u8ddd\u96e2 <code>d_sfm = \u2016A \u2212 B\u2016<\/code> \u3092\u8a08\u7b97<\/li>\n<li>\u73fe\u5b9f\u8ddd\u96e2 <code>d_real<\/code> \u3092\u6e2c\u5b9a<\/li>\n<li><code>scale = d_real \/ d_sfm<\/code> \u3092\u7b97\u51fa<\/li>\n<li>\u70b9\u7fa4\u5168\u9802\u70b9\u306b <code>scale<\/code> \u500d\u3092\u639b\u3051\u308b \u2192 <b>\u5b9f\u5bf8\u70b9\u7fa4<\/b> \u5b8c\u6210<\/li>\n<\/ol>\n\n<h3>2.2 \u6d41\u308c\u306e\u6982\u5ff5\u56f3<\/h3>\n<pre><code> \u250c\u2500\u2500 \u5199\u771f\u7fa4 \u2500\u2500\u25b6 SfM \u2500\u25b6 \u76f8\u5bfe\u70b9\u7fa4\n \u2502\n \u2502\uff08\u57fa\u6e96\u30de\u30fc\u30ab\u30fc\u306e\u5b9f\u8ddd\u96e2 d_real\uff09\n \u2502\n \u25bc\n\u57fa\u6e96\u70b9 A,B \u306e\u5ea7\u6a19\u53d6\u5f97 \u2500\u2500\u25b6 \u8ddd\u96e2 d_sfm \u8a08\u7b97\n \u2502\n \u25bc\n scale = d_real \/ d_sfm\n \u2502\n \u25bc\n\u70b9\u7fa4\u5168\u4f53\u3092 scale \u500d \u2500\u2500\u25b6 \u5b9f\u5bf8\u70b9\u7fa4\uff08scaled.ply\uff09\n<\/code><\/pre>\n\n\n<h2>3. \u306a\u305c\u5b9f\u5bf8\u304c\u5fc5\u8981\u304b\uff1f<\/h2>\n<ul>\n<li><b>BIM\/CAD \u9023\u643a\uff1a<\/b> \u67f1\u9593\u8ddd\u96e2\u30fb\u58c1\u539a\u306a\u3069 mm\uff5ecm \u7cbe\u5ea6\u3067\u5fc5\u8981<\/li>\n<li><b>\u65bd\u5de5\u30fb\u6570\u91cf\u8a08\u7b97\uff1a<\/b> \u4f53\u7a4d\u30fb\u9762\u7a4d\u304c\u72c2\u3046\u3068\u30b3\u30b9\u30c8\u7b97\u51fa\u304c\u7834\u7dbb<\/li>\n<li><b>\u30a4\u30f3\u30d5\u30e9\u70b9\u691c\uff1a<\/b> \u4e80\u88c2\u5e45\u30fb\u6c88\u4e0b\u91cf\u3092\u5b9f\u9577\u3067\u8ffd\u8de1<\/li>\n<li><b>\u30ea\u30d0\u30fc\u30b9\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\uff1a<\/b> \u90e8\u54c1\u5bf8\u6cd5\u305d\u306e\u3082\u306e\u304c\u6210\u679c\u7269<\/li>\n<\/ul>\n<div class=\"important\">\u30b9\u30b1\u30fc\u30eb\u672a\u4ed8\u4e0e\u70b9\u7fa4\u306f \u201c\u9451\u8cde\u7269\u201d\u3002<b>\u9053\u5177\u306b\u3059\u308b\u306a\u3089\u5fc5\u305a\u5b9f\u5bf8\u5316<\/b>\u3002<\/div>\n\n\n<h2>4. \u30b9\u30b1\u30fc\u30eb\u3092\u5931\u3046\u30e1\u30ab\u30cb\u30ba\u30e0<\/h2>\n<p>\u5199\u771f\u30c7\u30fc\u30bf\u3060\u3051\u3067\u306f\u30ab\u30e1\u30e9\u2010\u88ab\u5199\u4f53\u9593\u306e\u672c\u5f53\u306e\u8ddd\u96e2\u304c\u5206\u304b\u3089\u305a\u3001 \n\u305f\u3068\u3048\u7126\u70b9\u8ddd\u96e2\u30fb\u30bb\u30f3\u30b5\u30b5\u30a4\u30ba\u304c\u5206\u304b\u3063\u3066\u3082<b>\u201c\u500d\u7387\u201d \u306f\u4e00\u610f\u306b\u6c7a\u307e\u3089\u306a\u3044<\/b>\u305f\u3081\u3067\u3059\u3002<\/p>\n\n<div class=\"tips\"><b>\u89e3\u6c7a\u7b56<\/b>\uff1a\u64ae\u5f71\u6642\u306b\u57fa\u6e96\u30de\u30fc\u30ab\u30fc\u3092\u5199\u3057\u3001\u5f8c\u51e6\u7406\u3067\u30b9\u30b1\u30fc\u30eb\u500d\u7387\u3092\u639b\u3051\u308b\u3002<\/div>\n\n\n<h2>5. \u30b9\u30b1\u30fc\u30eb\u4ed8\u4e0e\u3060\u3051\u3092\u884c\u3046\u6700\u5c0f Python \u30b5\u30f3\u30d7\u30eb<\/h2>\n\n<div class=\"code-title\">scale_pointcloud.py<\/div>\n<pre><code>import numpy as np\n\ndef load_ply_points(ply_path):\n with open(ply_path) as f:\n lines = f.readlines()\n end = [i for i,l in enumerate(lines) if l.strip()==\"end_header\"][0]\n pts = [list(map(float,l.split()[:3])) for l in lines[end+1:] if l.strip()]\n return np.array(pts)\n\ndef save_ply_points(pts, out_path):\n with open(out_path,\"w\") as f:\n f.write(\"ply\\nformat ascii 1.0\\n\")\n f.write(f\"element vertex {len(pts)}\\n\")\n f.write(\"property float x\\nproperty float y\\nproperty float z\\nend_header\\n\")\n for p in pts: f.write(f\"{p[0]} {p[1]} {p[2]}\\n\")\n\ndef scale_pointcloud(ply_in, ply_out, idxA, idxB, real_d):\n pts = load_ply_points(ply_in)\n scale = real_d \/ np.linalg.norm(pts[idxA] - pts[idxB])\n save_ply_points(pts*scale, ply_out)\n print(f\"\u2192 wrote {ply_out} (scale={scale:.6f})\")\n\n# \u4f8b: scale_pointcloud(\"raw.ply\",\"scaled.ply\",10,20,1.50)\n<\/code><\/pre>\n\n\n<h2>6. \u5b8c\u5168\u81ea\u52d5\u5316\u30d1\u30a4\u30d7\u30e9\u30a4\u30f3\u69cb\u6210<\/h2>\n\n<table>\n<thead><tr><th>\u6bb5\u968e<\/th><th>\u30c4\u30fc\u30eb<\/th><th>\u5b9f\u88c5<\/th><th>\u5099\u8003<\/th><\/tr><\/thead>\n<tbody>\n<tr><td>\u7279\u5fb4\u62bd\u51fa\u301c\u5bc6\u70b9\u7fa4<\/td><td>COLMAP CLI + CUDA<\/td><td>Docker\/Bash<\/td><td>GPU \u30d8\u30c3\u30c9\u30ec\u30b9<\/td><\/tr>\n<tr><td>\u30b9\u30b1\u30fc\u30eb\u4ed8\u4e0e<\/td><td>Python + Open3D<\/td><td>\u95a2\u6570\u5316<\/td><td>\u524d\u7ae0\u30ed\u30b8\u30c3\u30af<\/td><\/tr>\n<tr><td>\u5236\u5fa1\u30fb\u76e3\u8996<\/td><td>Python<\/td><td>CLI<\/td><td>\u30ea\u30c8\u30e9\u30a4 & \u30ed\u30b0<\/td><\/tr>\n<\/tbody>\n<\/table>\n\n\n<h2>7. Docker \u30a4\u30e1\u30fc\u30b8<\/h2>\n\n<div class=\"code-title\">docker\/Dockerfile<\/div>\n<pre><code>FROM nvidia\/cuda:12.3.0-runtime-ubuntu22.04\nRUN apt-get update && apt-get install -y colmap python3 python3-pip \\\n && pip install open3d numpy tqdm\nWORKDIR \/workspace\nENTRYPOINT [\"\/bin\/bash\"]\n<\/code><\/pre>\n\n\n<h2>8. \u5199\u771f\u2192\u5b9f\u5bf8\u70b9\u7fa4 \u30ef\u30f3\u30b3\u30de\u30f3\u30c9\u30b9\u30af\u30ea\u30d7\u30c8<\/h2>\n\n<div class=\"code-title\">run_sfm.py<\/div>\n<pre><code>#!\/usr\/bin\/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nUsage: python run_sfm.py <image_dir> <marker_distance_m>\n\u2192 scaled.ply \u3092\u51fa\u529b\n\"\"\"\nimport subprocess, tempfile, shutil, sys, time\nfrom pathlib import Path\nimport numpy as np, open3d as o3d\n\nIMGDIR, REAL = Path(sys.argv[1]), float(sys.argv[2])\nPT = (0,1); RETRY = 3\ndef sh(cmd,c=None): print(\">>\",\" \".join(map(str,cmd))); subprocess.check_call(cmd,cwd=c)\n\ndef colmap(img,work):\n db=work\/\"db.db\"; sp=work\/\"sparse\"; de=work\/\"dense\"\n sh([\"colmap\",\"feature_extractor\",\"--database_path\",db,\"--image_path\",img,\"--SiftExtraction.gpu_index\",\"0\"])\n sh([\"colmap\",\"exhaustive_matcher\",\"--database_path\",db,\"--SiftMatching.gpu_index\",\"0\"])\n sp.mkdir(); sh([\"colmap\",\"mapper\",\"--database_path\",db,\"--image_path\",img,\"--output_path\",sp])\n model=next(sp.iterdir())\n sh([\"colmap\",\"image_undistorter\",\"--image_path\",img,\"--input_path\",model,\"--output_path\",de,\"--output_type\",\"COLMAP\"])\n sh([\"colmap\",\"patch_match_stereo\",\"--workspace_path\",de,\"--workspace_format\",\"COLMAP\"])\n sh([\"colmap\",\"stereo_fusion\",\"--workspace_path\",de,\"--workspace_format\",\"COLMAP\",\n \"--input_type\",\"geometric\",\"--output_path\",de\/\"fused.ply\"])\n return de\/\"fused.ply\"\n\ndef scale(ply_in,ply_out,idxA,idxB,real):\n pc=o3d.io.read_point_cloud(str(ply_in)); pts=np.asarray(pc.points)\n s=real\/np.linalg.norm(pts[idxA]-pts[idxB]); pc.scale(s,center=(0,0,0))\n o3d.io.write_point_cloud(str(ply_out),pc); print(f\"[OK] {ply_out} scale={s:.6f}\")\n\ndef main():\n work=Path(tempfile.mkdtemp(prefix=\"sfm_\")); print(\"TMP:\",work)\n for i in range(1,RETRY+1):\n try: fused=colmap(IMGDIR,work); break\n except subprocess.CalledProcessError: print(\"[WARN] retry\",i); time.sleep(5)\n scale(fused,Path(\"scaled.ply\"),PT[0],PT[1],REAL); shutil.rmtree(work)\n\nif __name__==\"__main__\":\n if len(sys.argv)<3: sys.exit(\"Usage: run_sfm.py <img_dir> <dist_m>\")\n main()\n<\/code><\/pre>\n\n\n<h2>9. \u5b9f\u884c\u4f8b\uff08Docker \u30b3\u30f3\u30c6\u30ca\uff09<\/h2>\n<pre><code>docker run --gpus all -it \\\n -v \/abs\/path\/to\/images:\/imgs \\\n -v $(pwd):\/out colmap-auto \\\n python3 \/workspace\/run_sfm.py \/imgs 1.50\n# \u2192 \/out\/scaled.ply \u304c\u751f\u6210\n<\/code><\/pre>\n\n\n<h2>10. \u62e1\u5f35\u30a2\u30a4\u30c7\u30a2 & \u30dc\u30c8\u30eb\u30cd\u30c3\u30af\u5bfe\u7b56<\/h2>\n<ul>\n<li>\u30d5\u30a9\u30eb\u30c0\u76e3\u8996\uff1a<code>watchdog<\/code> \u3067\u81ea\u52d5\u6295\u5165\uff0fSlack \u901a\u77e5<\/li>\n<li>\u30de\u30fc\u30ab\u30fc\u81ea\u52d5\u691c\u51fa\uff1a\u8272\u95be\u5024 or AprilTag \u2192 <code>PT<\/code> \u81ea\u52d5\u6c7a\u5b9a<\/li>\n<li>SIFT \u62bd\u51fa\u306f <code>--SiftExtraction.num_threads=$(nproc)<\/code><\/li>\n<li>\u5927\u898f\u6a21\u6848\u4ef6\u306f <code>vocab_tree_matcher<\/code> \u3067\u30de\u30c3\u30c1\u30f3\u30b0\u9ad8\u901f\u5316<\/li>\n<li>PatchMatch \u753b\u50cf\u30b5\u30a4\u30ba\u3092 GPU VRAM \u306b\u5408\u308f\u305b\u3066 3k\uff5e4k<\/li>\n<li>Docker \u5171\u6709\u30e1\u30e2\u30ea\u4e0d\u8db3\u306f <code>--shm-size 8G<\/code><\/li>\n<\/ul>\n\n\n<h2>11. \u307e\u3068\u3081<\/h2>\n<ul>\n<li>\u7d76\u5bfe\u30b9\u30b1\u30fc\u30eb\u306f SfM \u51e6\u7406\u3067\u5fc5\u305a\u5931\u308f\u308c\u308b\u2014<b>\u57fa\u6e96\u30de\u30fc\u30ab\u30fc<\/b> \u3068 <b>\u4e00\u62ec\u500d\u7387<\/b> \u3067\u89e3\u6c7a<\/li>\n<li>Python \u6570\u884c\u3067\u30b9\u30b1\u30fc\u30eb\u4ed8\u4e0e\u3001Docker\uff0bCOLMAP \u3067\u5b8c\u5168\u81ea\u52d5\u5316<\/li>\n<li>API \u5316\u30fb\u5e38\u99d0\u30b5\u30fc\u30d3\u30b9\u5316\u3067 \u201c\u73fe\u5834\u304b\u3089\u5199\u771f\u3092\u6295\u3052\u308b\u3060\u3051\u201d \u904b\u7528\u3078<\/li>\n<\/ul>\n\n<div class=\"important\"><b>\u5199\u771f \u2192 \u5b9f\u5bf8\u70b9\u7fa4 \u306e\u81ea\u52d5\u5316\u306f\u300c\u3084\u308b\u304b\u30fb\u3084\u3089\u306a\u3044\u304b\u300d\u3060\u3051\u3002<\/b><\/div>\n\n<hr>\n<small>(C) 2025 Bee-Knowledge Design \u2014 No-nonsense, copy-ready.<\/small>\n\n<\/body>\n<\/html>\n\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u5199\u771f\u2192\u5b9f\u5bf8\u70b9\u7fa4\uff1a\u7406\u8ad6\u3068\u5b8c\u5168\u81ea\u52d5\u5316\u30d1\u30a4\u30d7\u30e9\u30a4\u30f3 1. SfM \u306e\u6982\u8981\u3068\u51fa\u529b\u306e\u6027\u8cea \u672c\u6587\u7ae0\u306f\u57f7\u7b46\u9014\u4e2d\u306e\u3082\u306e\u3067\u3042\u308a\u3001\u30b3\u30fc\u30c9\u985e\u306e\u5b8c\u5168\u4fdd\u8a3c\u306f\u3067\u304d\u307e\u305b\u3093\u3002\u53c2\u8003\u7a0b\u5ea6\u306b\u304a\u8aad\u307f\u304f\u3060\u3055\u3044\u3002SfM\uff08Structure from Motion\uff09 […]<\/p>\n","protected":false},"author":1,"featured_media":1589,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"saved_in_kubio":false,"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"vkexunit_cta_each_option":"","footnotes":""},"categories":[6,60],"tags":[73,27,51,44,36,72],"class_list":["post-1581","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-programing","category-60","tag-73","tag-27","tag-51","tag-44","tag-36","tag-72"],"aioseo_notices":[],"jetpack_featured_media_url":"https:\/\/beeknowledge.co.jp\/wp-content\/uploads\/2025\/08\/00pxtcj6pxtcj.jpg","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts\/1581","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1581"}],"version-history":[{"count":8,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts\/1581\/revisions"}],"predecessor-version":[{"id":1590,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts\/1581\/revisions\/1590"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/media\/1589"}],"wp:attachment":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1581"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1581"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1581"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}