{"id":264,"date":"2025-03-19T14:40:04","date_gmt":"2025-03-19T14:40:04","guid":{"rendered":"http:\/\/www.maevewang.vip\/?p=264"},"modified":"2025-03-19T14:41:12","modified_gmt":"2025-03-19T14:41:12","slug":"tsegformer","status":"publish","type":"post","link":"http:\/\/www.maevewang.vip\/index.php\/2025\/03\/19\/tsegformer\/","title":{"rendered":"TSegFormer\u4ee3\u7801\u590d\u73b0"},"content":{"rendered":"\n<h3 class=\"wp-block-heading\"><strong>TSegFormer: 3D Tooth Segmentation in Intraoral Scans with Geometry Guided Transformer<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><code>\u8bba\u6587\u94fe\u63a5\uff1a<a href=\"http:\/\/www.maevewang.vip\/index.php\/2025\/03\/19\/tsegformer\/?url=https:\/\/arxiv.org\/pdf\/2311.13234\" >arxiv.org\/pdf\/2311.13234<\/a><\/code><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><code>github \u94fe\u63a5\uff1a<a href=\"http:\/\/www.maevewang.vip\/index.php\/2025\/03\/19\/tsegformer\/?url=https:\/\/github.com\/huiminxiong\/TSegFormer\" >huiminxiong\/TSegFormer: [MICCAI 2023] TSegFormer: 3D Tooth Segmentation in Intraoral Scans with Geometry Guided Transformer<\/a><\/code><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><code>\u4ee3\u7801\u590d\u73b0\u6bd4\u8f83\u56f0\u96be\u7684\u5730\u65b9\u662f\u6570\u636e\u96c6\u7684\u5904\u7406\uff0c\u56e0\u6b64\u4e0b\u9762\u4e3b\u8981\u4ecb\u7ecd\u6570\u636e\u96c6\u5904\u7406\u7684\u90e8\u5206<\/code><\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>\u4e2a\u4eba\u4f7f\u7528\u7684\u6570\u636e\u96c6<\/strong><\/h4>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"http:\/\/www.maevewang.vip\/index.php\/2025\/03\/19\/tsegformer\/?url=https:\/\/osf.io\/xctdy\/\" >OSF | Teeth3DS+<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u6570\u636e\u96c6\u9700\u8981\u6709\uff1a\u4e09\u7ef4\u70b9\u5750\u6807\u4ee5\u53ca\u70b9\u5bf9\u5e94\u7684\u6807\u7b7e<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>\u6570\u636e\u96c6\u5904\u7406<\/strong><\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">\u9996\u5148\u6839\u636e data.py \u548c\u8bba\u6587\uff0c\u6211\u4eec\u53ef\u4ee5\u5f97\u51fa\u6570\u636e\u96c6\u7684\u683c\u5f0f\uff1a<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">feature,8 \u7ef4\u5411\u91cf\uff0c\u5206\u522b\u662f 3 \u7ef4\u70b9\u5750\u6807+3 \u7ef4\u6cd5\u5411\u91cf+\u9ad8\u65af\u66f2\u7387+\u70b9&#8221;\u66f2\u7387&#8221;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">label,\u6807\u7b7e\uff0c0-32\uff0c0 \u662f\u7259\u9f88<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">category,(1,0)\u4e3a\u4e0b\u988c\u9aa8\uff0c(0,1)\u4e3a\u4e0a\u988c\u9aa8<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=\"http:\/\/www.maevewang.vip\/wp-content\/uploads\/2025\/03\/image-1.png\" rel=\"box\" class=\"fancybox\"><img loading=\"lazy\" decoding=\"async\" width=\"2067\" height=\"769\" src=\"http:\/\/www.maevewang.vip\/wp-content\/uploads\/2025\/03\/image-1.png\" alt=\"\" class=\"wp-image-266\"\/><\/a><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">\u5177\u4f53\u5b9e\u73b0\u5982\u4e0b\uff1a<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">1.\u4ece.obj \u6587\u4ef6\u4e2d\u63d0\u53d6\u51fa\u70b9\u5750\u6807<\/h5>\n\n\n\n<pre class=\"wp-block-code\"><code>def read_obj_vertices(file_path):  \n    vertices = &#91;] \n    with open(file_path, 'r') as file:  \n        for line in file: \n            if line.startswith('v '):  # \u4ec5\u5904\u7406\u9876\u70b9\u884c  \n                parts = line.strip().split()   \n                x, y, z = map(float, parts&#91;1:4])  # \u63d0\u53d6 x,y,z \u5750\u6807  \n                vertices.append(&#91;x,  y, z])  \n    return np.array(vertices)<\/code><\/pre>\n\n\n\n<h5 class=\"wp-block-heading\">2.\u8ba1\u7b97\u6cd5\u7ebf\uff0c\u8fd9\u91cc\u901a\u8fc7 open3d \u8ba1\u7b97<\/h5>\n\n\n\n<pre class=\"wp-block-code\"><code>pcd = o3d.geometry.PointCloud()\npcd.points  = o3d.utility.Vector3dVector(vertex_array)\npcd.estimate_normals( search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=5.5,  max_nn=30))\nnormals_array = np.asarray(pcd.normals)<\/code><\/pre>\n\n\n\n<h5 class=\"wp-block-heading\">3.\u8ba1\u7b97\u9ad8\u65af\u66f2\u7387<\/h5>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-size:0.8rem\">Deepseek \u751f\u6210\u7684(\u4e0d\u4fdd\u8bc1\u6b63\u786e\u6027)\uff0c\u6700\u540e\u6570\u636e\u5f88\u5927\uff0c\u6240\u4ee5\u5728\u6700\u540e\u5904\u7406\u65f6\uff0c\u6240\u6709\u7684\u6570\u636e\u8fdb\u884c\u4e86\u5f52\u4e00\u5316\u5904\u7406<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>def compute_gaussian_curvature(points, normals, radius=0.1):\n    \"\"\"\n    \u8ba1\u7b97\u70b9\u4e91\u4e2d\u6bcf\u4e2a\u70b9\u7684\u9ad8\u65af\u66f2\u7387\n    \n    \u53c2\u6570:\n        points: numpy \u6570\u7ec4\uff0c\u5f62\u72b6\u4e3a(N, 3)\uff0c\u8868\u793a\u70b9\u4e91\u4e2d\u7684\u70b9\n        normals: numpy \u6570\u7ec4\uff0c\u5f62\u72b6\u4e3a(N, 3)\uff0c\u8868\u793a\u6bcf\u4e2a\u70b9\u7684\u6cd5\u5411\u91cf\n        radius: \u6d6e\u70b9\u6570\uff0c\u7528\u4e8e\u786e\u5b9a\u90bb\u57df\u8303\u56f4\u7684\u534a\u5f84\n        \n    \u8fd4\u56de:\n        gaussian_curvatures: numpy \u6570\u7ec4\uff0c\u5f62\u72b6\u4e3a(N,)\uff0c\u6bcf\u4e2a\u70b9\u7684\u9ad8\u65af\u66f2\u7387\n    \"\"\"\n    # \u6784\u5efa KD \u6811\u4ee5\u5feb\u901f\u67e5\u627e\u90bb\u57df\u70b9\n    tree = KDTree(points)\n    \n    gaussian_curvatures = np.zeros(points.shape&#91;0])\n    \n    for i in range(points.shape&#91;0]):\n        # \u627e\u5230\u4ee5\u5f53\u524d\u70b9\u4e3a\u4e2d\u5fc3\uff0c\u534a\u5f84\u4e3a radius \u7684\u90bb\u57df\u5185\u7684\u70b9\u7684\u7d22\u5f15\n        indices = tree.query_ball_point(points&#91;i], radius)\n        neighborhood_points = points&#91;indices]\n        \n        # \u5982\u679c\u90bb\u57df\u5185\u70b9\u6570\u4e0d\u8db3\uff0c\u8df3\u8fc7\n        if len(neighborhood_points) &lt; 3:\n            gaussian_curvatures&#91;i] = 0\n            continue\n        \n        # \u8ba1\u7b97\u534f\u65b9\u5dee\u77e9\u9635\n        mean_point = np.mean(neighborhood_points, axis=0)\n        centered_points = neighborhood_points - mean_point\n        cov_matrix = np.dot(centered_points.T, centered_points)\n        \n        # \u8ba1\u7b97\u534f\u65b9\u5dee\u77e9\u9635\u7684\u7279\u5f81\u503c\n        eigenvalues, _ = np.linalg.eig(cov_matrix)\n        eigenvalues = np.sort(eigenvalues)&#91;::-1]\n        \n        # \u8ba1\u7b97\u4e3b\u66f2\u7387\uff08\u8fd9\u91cc\u7b80\u5316\u5904\u7406\uff0c\u5b9e\u9645\u53ef\u80fd\u9700\u8981\u66f4\u590d\u6742\u7684\u66f2\u9762\u62df\u5408\uff09\n        k1 = eigenvalues&#91;0]\n        k2 = eigenvalues&#91;1]\n        \n        # \u9ad8\u65af\u66f2\u7387\u662f\u4e3b\u66f2\u7387\u7684\u4e58\u79ef\n        gaussian_curvatures&#91;i] = k1 * k2\n    \n    return gaussian_curvatures<\/code><\/pre>\n\n\n\n<h5 class=\"wp-block-heading\">4.\u8ba1\u7b97\u70b9\u201c\u66f2\u7387\u201d<\/h5>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-size:0.8rem\">Deepseek \u751f\u6210\u7684(\u4e0d\u4fdd\u8bc1\u6b63\u786e\u6027)<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>def compute_point_curvature_new(points, normals, radius=0.1): \n    \"\"\" \n    \u8ba1\u7b97\u70b9\u4e91\u4e2d\u6bcf\u4e2a\u70b9\u7684\u65b0\u5b9a\u4e49\u7684\u70b9\u201c\u66f2\u7387\u201d \n    \n    \u53c2\u6570: \n    points (numpy.ndarray):  \u70b9\u4e91\u6570\u636e\uff0c\u5f62\u72b6\u4e3a (n_points, 3)\uff0c\u5176\u4e2d n_points \u662f\u70b9\u7684\u6570\u91cf \n    normals (numpy.ndarray):  \u70b9\u4e91\u7684\u6cd5\u5411\u91cf\uff0c\u5f62\u72b6\u4e3a (n_points, 3) \n    radius (float): \u90bb\u57df\u534a\u5f84\uff0c\u9ed8\u8ba4\u4e3a 0.1 \n    \n    \u8fd4\u56de: \n    numpy.ndarray:  \u6bcf\u4e2a\u70b9\u7684\u66f2\u7387\uff0c\u5f62\u72b6\u4e3a (n_points,) \n    \"\"\" \n    n_points = points.shape&#91;0]  \n    curvatures = np.zeros(n_points)  \n    \n    # \u4f7f\u7528 sklearn \u7684 NearestNeighbors \u6765\u67e5\u627e\u6bcf\u4e2a\u70b9\u7684\u90bb\u57df \n    nbrs = NearestNeighbors(radius=radius, algorithm='ball_tree').fit(points) \n    \n    for i in range(n_points): \n        # \u67e5\u627e\u5f53\u524d\u70b9\u7684\u90bb\u57df\u70b9 \n        distances, indices = nbrs.radius_neighbors(&#91;points&#91;i]],  return_distance=True) \n        neighbor_indices = indices&#91;0] \n        \n        if len(neighbor_indices) > 1: \n            # \u83b7\u53d6\u5f53\u524d\u70b9\u7684\u6cd5\u5411\u91cf \n            current_normal = normals&#91;i] \n            \n            # \u83b7\u53d6\u90bb\u57df\u70b9\u7684\u6cd5\u5411\u91cf \n            neighbor_normals = normals&#91;neighbor_indices] \n            \n            # \u8ba1\u7b97\u5f53\u524d\u70b9\u6cd5\u5411\u91cf\u4e0e\u90bb\u57df\u70b9\u6cd5\u5411\u91cf\u7684\u5939\u89d2\u4f59\u5f26\u503c \n            cos_angles = np.dot(neighbor_normals,  current_normal) \n            \n            # \u8ba1\u7b97\u66f2\u7387\uff0c\u8fd9\u91cc\u5b9a\u4e49\u4e3a\u5939\u89d2\u4f59\u5f26\u503c\u7684\u5e73\u5747\u503c \n            curvature = np.mean(cos_angles)  \n            curvatures&#91;i] = curvature \n    \n    return curvatures <\/code><\/pre>\n\n\n\n<h5 class=\"wp-block-heading\">5.\u6700\u7ec8\u5f97\u5230\u7684\u6570\u636e\u8fdb\u884c\u5f52\u4e00\u5316\u5904\u7406<\/h5>\n\n\n\n<pre class=\"wp-block-code\"><code>def pc_normalize(pc):\n    centroid = np.mean(pc, axis=0)\n    pc = pc - centroid\n    m = np.max(np.sqrt(np.sum(pc ** 2, axis=1)))\n    pc = pc \/ m\n    return pc\n\nvertices = pc_normalize(vertices)\nnormals = pc_normalize(normals)\ngaussian_curvatures = gaussian_curvatures \/ 10000000000<\/code><\/pre>\n\n\n\n<h5 class=\"wp-block-heading\">6.labels \u8f6c\u6362<\/h5>\n\n\n\n<pre class=\"wp-block-code\"><code>def number_covert(original):\n    num_map = {\n        31:1, \n        32:2, \n        33:3, \n        34:4, \n        35:5, \n        36:6,\n        37:7,\n        38:8,\n        41:9, \n        42:10, \n        43:11, \n        44:12, \n        45:13, \n        46:14, \n        47:15,\n        48:16,\n        11:17,\n        12:18,\n        13:19,\n        14:20,\n        15:21,\n        16:22,\n        17:23,\n        18:24,\n        21:25,\n        22:26,\n        23:27,\n        24:28,\n        25:29,\n        26:30,\n        27:31,\n        28:32\n    }\n    new_list = &#91;num_map.get(x,  x) for x in original]\n    return new_list<\/code><\/pre>\n\n\n\n<h5 class=\"wp-block-heading\">7.\u4fee\u6539 data.py<\/h5>\n\n\n\n<pre class=\"wp-block-code\"><code>def data_load(DATA_PATH):\n    \"\"\"\n    According to the path, load the teeth data from the preprocessed json file.\n    Return: feature (8-d vector), label (int:0-32), category ((1, 0) for mandible \/ (0, 1) for maxillary)\n    \"\"\"\n    f = open(DATA_PATH + \".json\", 'r')\n    teeth_dict = json.load(f)\n    label = teeth_dict&#91;'labels']\n    f.close()    \n    \n    label = number_covert(label)\n    label = np.array(label).astype(np.int64)\n    # print(label)\n    cat = teeth_dict&#91;'jaw']\n    if cat == 'lower':\n        category = (1, 0)\n    else:\n        category = (0, 1)\n    category = np.array(category).astype(np.float32)\n    # print(category)\n\n        feature = np.concatenate((vertices, normals), axis=1)\n        feature  = np.concatenate((feature, gaussian_array), axis=1)\n        feature  = np.concatenate((feature, curvature_array), axis=1)\n            \n    # print(feature)\n    return feature, label, category<\/code><\/pre>\n\n\n\n<h5 class=\"wp-block-heading\">8.\u8fdb\u884c\u8bad\u7ec3<\/h5>\n\n\n\n<pre class=\"wp-block-code\"><code>python main.py --epochs 200 --num_points 10000<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>TSegFormer: 3D Tooth Segmentation in Intraoral Scans wi [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":266,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-264","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-my_major"],"_links":{"self":[{"href":"http:\/\/www.maevewang.vip\/index.php\/wp-json\/wp\/v2\/posts\/264","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.maevewang.vip\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.maevewang.vip\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.maevewang.vip\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.maevewang.vip\/index.php\/wp-json\/wp\/v2\/comments?post=264"}],"version-history":[{"count":2,"href":"http:\/\/www.maevewang.vip\/index.php\/wp-json\/wp\/v2\/posts\/264\/revisions"}],"predecessor-version":[{"id":268,"href":"http:\/\/www.maevewang.vip\/index.php\/wp-json\/wp\/v2\/posts\/264\/revisions\/268"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/www.maevewang.vip\/index.php\/wp-json\/wp\/v2\/media\/266"}],"wp:attachment":[{"href":"http:\/\/www.maevewang.vip\/index.php\/wp-json\/wp\/v2\/media?parent=264"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.maevewang.vip\/index.php\/wp-json\/wp\/v2\/categories?post=264"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.maevewang.vip\/index.php\/wp-json\/wp\/v2\/tags?post=264"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}