diff --git a/10/3.py b/10/3.py
index cdb6728..b869ac9 100644
--- a/10/3.py
+++ b/10/3.py
@@ -1,26 +1,19 @@
-# 利用OpenCV实现两张2D图像的3D重构。
-
 import cv2
 import numpy as np
 import plotly.express as px
 import plotly.io as pio
 
-# 设置plotly在浏览器中显示
 pio.renderers.default = "browser"
 
-# 读取图像
 img1 = cv2.imread(r"10\templeRing\templeR0003.png")
 img2 = cv2.imread(r"10\templeRing\templeR0005.png")
 
-# 定义相机内参
 fx = 0.25 * 1520.4
 fy = 0.25 * 1525.9
 cx = 302.32
 cy = 246.87
-
 K = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])
 
-# 定义第一个相机的外参矩阵
 R1 = np.array(
     [
         [-0.01625331773280620100, 0.98386957700862299000, -0.17814736905031653000],
@@ -30,7 +23,6 @@ R1 = np.array(
 )
 t1 = np.array([[-0.0283090812583], [-0.0366442193256], [0.529139415773]])
 
-# 定义第二个相机的外参矩阵
 R2 = np.array(
     [
         [-0.05235090589954815400, 0.98479784589115438000, -0.16562785206491965000],
@@ -40,65 +32,48 @@ R2 = np.array(
 )
 t2 = np.array([[-0.0269600886818], [-0.0469344855587], [0.53860946783]])
 
-# 转换图像为灰度图
 gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
 gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
 
-# 创建SIFT特征检测器
 sift = cv2.SIFT_create()
 
-# 检测特征点和计算描述符
 kp1, des1 = sift.detectAndCompute(gray1, None)
 kp2, des2 = sift.detectAndCompute(gray2, None)
 
-# 创建BFMatcher对象
 bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)
 
-# 进行特征匹配
 matches = bf.match(des1, des2)
 
-# 按距离排序
 matches = sorted(matches, key=lambda x: x.distance)
 
-# 选择前N个最佳匹配点（可以调整这个数值）
 N_BEST_MATCHES = 100
 matches = matches[:N_BEST_MATCHES]
 
-# 提取匹配点的坐标
 pts1 = np.float32([kp1[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)
 pts2 = np.float32([kp2[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)
 
-# 计算投影矩阵
 P1 = K @ np.hstack((R1, t1))
 P2 = K @ np.hstack((R2, t2))
 
-# 三角测量得到3D点
 points_4D = cv2.triangulatePoints(P1, P2, pts1, pts2)
 points_3D = points_4D / points_4D[3]
 points_3D = points_3D[:3, :].T
 
-# 可选：移除一些可能的异常点
-# 这里使用简单的距离阈值进行过滤
 distances = np.sqrt(np.sum(points_3D**2, axis=1))
-mask = distances < np.percentile(distances, 95)  # 移除最远的5%的点
+mask = distances < np.percentile(distances, 95)
 points_3D = points_3D[mask]
 
-# 使用plotly进行3D可视化
 fig = px.scatter_3d(
     x=points_3D[:, 0], y=points_3D[:, 1], z=points_3D[:, 2], title="3D Reconstruction"
 )
 
-# 调整点的大小和视角
 fig.update_traces(marker=dict(size=1))
-fig.update_layout(scene=dict(aspectmode="data"))  # 保持真实比例
+fig.update_layout(scene=dict(aspectmode="data"))
 
-# 显示结果
 fig.show()
 
-# 保存结果
 fig.write_html("10/templeR.html")
 
-# 可选：显示匹配结果
 img_matches = cv2.drawMatches(
     img1,
     kp1,