GitHub - yihui-he/3D-reconstruction: two view structure from motion

1. You can directly go to result folder to see all results

2. To make it easier to view all results , I selected two images for each imageset. You can reproduce all 3D models using main.m. All 8 pclouds will show up together after program finished. .ply files will be saved to result folder, which you can be opened with meshlab. main;

3. You can specify two images(intrinsic.new must be in the same folder):

SfM2('imgFolder/img1.JPG','imgFolder/img2.JPG');

will not show model after finished, only save .ply to result.

SfM2('imgFolder/img1.JPG','imgFolder/img2.JPG',true);

will show model after finished, and save .ply to result.

• dense matching.
• MSAC instead of RANSAC.

1. get camera intrinsic matrix.
2. features detection and points matching.
3. estimate fundamental matrix using feature pairs in two images. Then compute essential matrix using K and F. Decompose E to R and t. Get P using E.
4. dense matching.
5. put pairs of points onto 3D(triangulate).

• prepare 2 images taken from two different views
• get the intrinsic matrix and write it to intrinsic.new

https://wikimedia.org/api/rest_v1/media/math/render/svg/a73c022621ea3e7546d2a95c22a74fb22a3b3b7c

You can set parameters except $\alpha_x$ and $\alpha_y$ can be default value: zero,

https://wikimedia.org/api/rest_v1/media/math/render/svg/3f0b99ce362b84c94a603bca45c11454cb95f6f1

,

https://wikimedia.org/api/rest_v1/media/math/render/svg/eb5fb4f7aef1abe7c21500f0486677fec1e2ceca

, represent focal length in terms of pixels, where $m_x$, $m_y$ are the scale factors relating pixels to distance and f is the focal length in terms of distance. They can be obtained by looking into your camera info or the jpeg meta info. You can google the way to get them.

• Put them into the same folder, then modify main.m to point to the images