![]() Then you'll see that you actually have a point cloud, not a textured mesh. For a start, you could change the focal length to shrink the picture or enlarge it, then try moving the camera into the scene a little bit. When you actually change the viewpoint, you'll see a lot clearer what's going on. The gaps you see are an aliasing effect (from the truncation), related to moiré patterns. It will give you less error in your results, and it should work well for the case of no viewpoint change. ![]() You should round before converting: x, y = point.round().astype(int) Truncation results in 6.0, and now you've got almost an entire pixel of error. Say a coordinate is calculated to be 6.999999. Your x, y = point.astype(int) is always truncating. Small numerical errors happen (and accumulate) due to the finite precision of numbers. Download Inkscape from the Inkscape website, then open the application. We will begin by opening the image we want to convert into a 3D model in Photoshop. Download the Inkscape logo and save the image to your computer. To start with this process we will use Photoshop or Illustrator, either software is valid. ![]() To follow along with this tutorial, download and work with a copy of the Inkscape logo as an example. The gaps between your individually drawn points are due to numerics. The first step in turning a 2D image into a 3D model is to create an SVG file. That can be drawn nicely with any 2D/3D graphics library (OpenGL, D3D. You would need to have a textured mesh instead. You are projecting each point and then drawing it as a pixel. Synthesized_image = left_imageĬv2.imshow("Synthesized Image", synthesized_image)Īnd the 2D-3D-2D image I get using the code: If 0 <= x < synthesized_image.shape and 0 <= y < synthesized_image.shape: # Copy RGB values from the original image to the synthesized image based on the reprojected 2D coordinatesįor i, point in enumerate(reprojected_2d): Synthesized_image = np.zeros_like(left_image) # Create a blank image with the same size and channels as the original image Left_image = cv2.imread('Photometric_Stereo\data\example\im_left.jpg') ![]() Reprojected_2d = np.array(reprojected_2d) # Convert the lists of points to NumPy arrays Point_3d = np.linalg.inv(K) np.array() * depth # Replace the missing depth values with zero In addition, you can learn the basics of 3D solid modeling using only 10. Left_depth_map = np.loadtxt('Photometric_Stereo\data\example\depth_left.txt', delimiter=',') With the 3D modeling workspace in AutoCAD, you can convert a 2D design into a 3D model. Here is the code I'm using in Python: # Load depth map I have an image with its depth map and intrinsics matrix K, I'm trying to do 2D to 3D reprojection of the image using the camera matrix formula, I have done the reprojection, but to make sure that I did it correctly I'm trying to reproject the image from 3D to 2D to verify that the reprojection is being done correctly, but I get an image with a lot of distortion lines, horizontally and vertically, I have doubt that these distortion lines are coming from 0 values in the depth map but I'm not sure. ![]()
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