在两个相机之间找到用于“立体视觉化”的旋转矩阵。 [英] Finding Rotation matrices between two cameras for "Stereorectify"

问题描述

所以我有一个深度图，摄像机的外在和内在性。我想回到3D点和表面法线。我使用的功能 ReprojectImageTo3D 。在立体声纠正功能中找到Q如何获取旋转矩阵
之间
第一和第二摄像机的坐标系？我有个别旋转矩阵和平移向量，但我怎么得到它之间的相机之间？

So I have a depth map and the extrinsics and intrinsics of the camera.I want to get back the 3D points and the surface normals .I am using the functionReprojectImageTo3D.In the stereo rectify function to find Q how do I get the The rotation matrix between the 1st and the 2nd cameras’ coordinate systems? I have individual rotation matrix and translation vector but how do I get it for "between the cameras?" .Also this would give me the 3D points .Is there a method to generate the surface normals?

推荐答案

给出一个方法来生成表面法线你有两个摄像机的外在矩阵，你不能简单地取相机1的逆外部矩阵，乘以相机2的外在矩阵？
此外，对于两个摄像机之间的直接关系，看看基本矩阵（或更具体地，基本矩阵）。看看你是否可以找到Hartley和Zisserman的多视图几何图形的副本。

Given that you have the extrinsic matrix of both cameras, can't you simply take the inverse extrinsic matrix of camera 1, multiplied by the extrinsic matrix of camera 2? Also, for a direct relation between the two cameras, take a look at the Fundamental Matrix (or, more specific, the Essential matrix). See if you can find a copy of the book Multiple View Geometry by Hartley and Zisserman.

对于表面法线，你可以通过计算角上的交叉积的三角形。然而，您首先需要重建的3D点云。

As for the surface normals, you can compute those yourself by computing crossproducts on the corners of triangles. However, you then first need the reconstructed 3D point cloud.

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