In stereo, we have multiple cameras and in that case we need to convert the points from one camera to another since there will be multiple camera spaces. The reason I took rotation and translation as zero is that there is a single camera which means all the 3d points will be projected using that cameras' information. Then, you simply multiply the camera intrinsic with the found extrinsic matrix to find the projection matrix. You need to merge rotation (R) and translation (T) as seen in the figure which forms a (3x4) matrix. How can I convert 3D point to Range image using these transformation in matlab. shot rec.shots image pt2D shot.project (pt3D) pt2Dpx cam.normalizedtopixelcoordinates (pt2D) However, I did not manage to find the suitable methods to map a 2D pixel in the original image to the corresponding point in the 3D point cloud. Also, you can assume that the translation is also zero in all directions which is a vector containing zeros. 3d point cloud to 2d image python I can convert the data points into an. However, you can assume that the rotation in all axes is zero which will give you the identity matrix as the rotation matrix (you can just use 3x3 identity matrix but for other angles, you can find the rotation matrix with OpenCV's Rodriguez function). To find the projection matrix, you need to know the camera intrinsic matrix (3x3), rotation matrix (3x3), and the translation vector (3x1). If the 2d points are the correspondences of those 3d points in the camera space (where camera is the origin), then you can find the projection matrix.
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