Detection of lines in a point cloud What are the best solutions to detect line in a point cloud? Comparison being made with and between Hough Transform, Radon Transform, RANSAC (see wikipedia) and Brute-Force Search (see wikipedia).
Which one is the most robust against the dispersion of points in point cloud?
Note:
1- The question is about 3D point cloud not image.
2- Points in point cloud are randomly dispersed (completely sparse locations).
3- There is no information about the object (line) being explored in terms of preferred orientation, size, etc.
4- A tolerance needs to be considered around the candidate line.
Updates:
According to my experiments: RANSAC could miss easily some lines. It is good for quick detection of edges however the complexity of point dispersion could produce undesired outputs. Hough and Radon are very similar and I had not chance to try for 3D point cloud however they work well on 2D cases. There is a difficulty in extraction of segments of found lines. BFS is simply impractical for large data set.
It really depends, how will you measure the quality of the solutions? what are your requirements, real time, high accuracy? how large is the point cloud?

You mentioned valid, yet fancy signal processing based methods to address the problem. 

Let me add three methods you have not mentioned that are classic and more statistical in nature: least squares, ridge regression and lasso.