深度图转为点云说白了其实就是坐标系的变换：图像坐标系－－>世界坐标系。变换的约束条件就是相机内参，公式很简单：
,其中x,y,z是点云坐标系，x',y'是图像坐标系，Ｄ为深度值。
在进行上述转换之前必须对x',y'进行undistort（反畸变）运算，即便如此最终得到的点云数据还是存在误差的。棋盘标定出的内参本身是带有误差的，它是一种近似逼近值，也就是说它不能真实的映射相机内部结构。
作者：崔世界
链接：https://www.zhihu.com/question/268488496/answer/353439189
来源：知乎
网上一个版本的转换：
http://ziyedy.top/page/python-depth-points.html
def depth2pc(depth_img):
"""
深度图转点云数据
图像坐标系 -> 世界坐标系
:param depth_img: 深度图
:return: 点云数据 N*3
"""
# 相机内参
cam_fx = 1120.12
cam_fy = 1120.12
cam_cx = 640.5
cam_cy = 360.5
factor = 1
# 逐点处理，此过程可以使用numpy优化
m, n = depth_img.shape
point_cloud = []
for v in range(m):
for u in range(n):
if depth_img[v, u] == 0:
continue
depth = depth_img[v, u]
p_z = depth / factor
p_x = (u - cam_cx) * p_z / cam_fx
p_y = (v - cam_cy) * p_z / cam_fy
point_cloud.append([p_x, p_y, p_z])
point_cloud = np.array(point_cloud)
return point_cloud
最近做实验，想实现深度估计到三维点云再到网格的生成，第一步做出深度图结合RGB图生成三维点云
RGB室内图：
Depth Image：（隐隐约约能过看到点什么）
生成的结果：
转个角度：
结果很粗糙，感觉这个方法不是很理想，也就是通过深度估计得到的深度图转换成点云的想法不太靠谱！
代码如下：（需要安装open3d）
from PIL import Image
import pandas as pd
import numpy as np
from open3d import read_point_cloud, draw_geometries
import time
class point_cloud_generator():
def __init__(self, rgb_file, depth_file, pc_file, focal_length, scalingfactor):
self.rgb_file = rgb_file
self.depth_file = depth_file
self.pc_file = pc_file
self.focal_length = focal_length
self.scalingfactor = scalingfactor
self.rgb = Image.open(rgb_file)
self.depth = Image.open(depth_file).convert('I')
self.width = self.rgb.size[0]
self.height = self.rgb.size[1]
def calculate(self):
t1=time.time()
depth = np.asarray(self.depth).T
self.Z = depth / self.scalingfactor
X = np.zeros((self.width, self.height))
Y = np.zeros((self.width, self.height))
for i in range(self.width):
X[i, :] = np.full(X.shape[1], i)
self.X = ((X - self.width / 2) * self.Z) / self.focal_length
for i in range(self.height):
Y[:, i] = np.full(Y.shape[0], i)
self.Y = ((Y - self.height / 2) * self.Z) / self.focal_length
df=np.zeros((6,self.width*self.height))
df[0] = self.X.T.reshape(-1)
df[1] = -self.Y.T.reshape(-1)
df[2] = -self.Z.T.reshape(-1)
img = np.array(self.rgb)
df[3] = img[:, :, 0:1].reshape(-1)
df[4] = img[:, :, 1:2].reshape(-1)
df[5] = img[:, :, 2:3].reshape(-1)
self.df=df
t2=time.time()
print('calcualte 3d point cloud Done.',t2-t1)
def write_ply(self):
t1=time.time()
float_formatter = lambda x: "%.4f" % x
points =[]
for i in self.df.T:
points.append("{} {} {} {} {} {} 0\n".format
(float_formatter(i[0]), float_formatter(i[1]), float_formatter(i[2]),
int(i[3]), int(i[4]), int(i[5])))
file = open(self.pc_file, "w")
file.write('''ply
format ascii 1.0
element vertex %d
property float x
property float y
property float z
property uchar red
property uchar green
property uchar blue
property uchar alpha
end_header
%s
''' % (len(points), "".join(points)))
file.close()
t2=time.time()
print("Write into .ply file Done.",t2-t1)
def show_point_cloud(self):
pcd = read_point_cloud(self.pc_file)
draw_geometries([pcd])
a = point_cloud_generator('01446_colors.png', '01446_depth.png', '01446_3d.ply',
focal_length=300, scalingfactor=1000)
a.calculate()
a.write_ply()
a.show_point_cloud()
df = a.df
np.save('pc.npy',df)
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