Hi Vishwas,
I understand that you are looking to colorize a point cloud obtained from a mobile mapping LiDAR scanner using images captured from a GoPro camera. The goal is to project the images onto the point cloud and assign colors to the corresponding 3D points.
Here are the steps you can follow to perform point cloud colorization using multiple images in MATLAB:
1. Load the point cloud data: Read the point cloud data from your mobile mapping LiDAR scanner into MATLAB. You can use the ‘pcread()’ function from the Computer Vision Toolbox to read common point cloud file formats such as .ply or .xyz.
ptCloud = pcread('path_to_point_cloud_file.ply');
2. Load the images: Load the images captured from the GoPro camera into MATLAB. You can use the ‘imread()’ function to read the images.
image1 = imread('path_to_image1.jpg');
image2 = imread('path_to_image2.jpg');
% Load other images as needed
3. Extract features from the images: Use a feature detection algorithm such as SURF, SIFT, or ORB to extract keypoints and descriptors from the images. You can use the ‘detectSURFFeatures()’ function from the Computer Vision Toolbox to detect SURF features.
points1 = detectSURFFeatures(rgb2gray(image1));
points2 = detectSURFFeatures(rgb2gray(image2));
% Detect features in other images as needed
4. Match features across images: Perform feature matching to find correspondences between keypoints in different images. You can use the ‘matchFeatures()’ function from the Computer Vision Toolbox to match features based on their descriptors.
[features1, validPoints1] = extractFeatures(rgb2gray(image1), points1);
[features2, validPoints2] = extractFeatures(rgb2gray(image2), points2);
indexPairs = matchFeatures(features1, features2);
% Match features in other images as needed
5. Triangulate 3D points: Triangulate the matched feature correspondences to obtain 3D points in the world coordinate system. You can use the ‘triangulate()’ function from the Computer Vision Toolbox to perform triangulation.
worldPoints = triangulate(validPoints1(indexPairs(:, 1)), validPoints2(indexPairs(:, 2)), stereoParams);
% Triangulate points using other image pairs as needed
6. Assign colors to 3D points: Project the images onto the point cloud using the camera poses and assign the corresponding colors to the 3D points. You can use the ‘world2img()’ function from the Computer Vision Toolbox to project the points onto the images.
% Project points onto image1
projectedPoints1 = world2img(cameraParams1, worldPoints);
% Project points onto image2
projectedPoints2 = world2img(cameraParams2, worldPoints);
% Get the corresponding colors from image1
color1 = impixel(image1, projectedPoints1(:, 1), projectedPoints1(:, 2));
% Get the corresponding colors from image2
color2 = impixel(image2, projectedPoints2(:, 1), projectedPoints2(:, 2));
% Assign colors to the 3D points
coloredPoints = [worldPoints, color1, color2];
7. Combine colors from multiple images: Combine the colors obtained from multiple images for each 3D point. You can use various methods such as blending the colors or selecting the color with the highest confidence.
combinedColor = (color1 + color2) / 2; % Simple blending example
% Combine colors from other images as needed
8. Visualize the colored point cloud: Plot the colored point cloud using the ‘pcshow()’ function from the Computer Vision Toolbox.
ptCloud.Color = combinedColor;
pcshow(ptCloud);
Make sure to replace `'path_to_point_cloud_file.ply'`, `'path_to_image1.jpg'`, `'path_to_image2.jpg'`, etc., with the actual paths to your point cloud file and image files. Adjust the code according to the specific camera parameters and calibration data you have.
Please refer to the following MATLAB Documentation to know more about Image Processing and Computer Vision Toolboxes
Hope the above solution helps you.
Best Regards,
Aman Banthia
