Closed tigerdhl closed 6 months ago
Sorry I don't know much about feature matching. Do you think it can stitch the images in real time?
Sorry I don't know much about feature matching. Do you think it can stitch the images in real time?
What I mean is how the two corrected images from adjacent fisheye cameras are spliced together. I see that your documentation directly talks about the transition problem after splicing.
They can match together because, at the projection calibration step, you specified the parameters to project the images.
They can match together because, at the projection calibration step, you specified the parameters to project the images.
ok,I get it. For example,the four pixel points in the BEV image is Well designed?
You need to choose >=4 points to make sure your projected image covers the right region on the ground. The feature-matching method should also work as long as it can give the correct projection matrix. However, I'm not sure how well that method behaves because the projected images are taken by the front and left cameras respectively, they are not taken by the same camera from different viewpoints. So I doubt a more advanced method should be used here.
Thank you for sharing. But there is a question that I don’t understand. For example, when splicing two images, top and left, how the grids can be aligned neatly, the usual method is through feature matching, but the author does not seem to mention this step, only smoothing the splicing after the splicing is completed. @neozhaoliang