But the real world is neither clean nor obedient.
If you wanted to know the height of a doorway, the width of a warehouse, or the distance between two streetlamps, you needed a physical tool: a laser, a tape measure, or at least a stereo camera rig. Then came the constraint of "controlled environments." Labs with checkerboard patterns. Studios with calibrated lighting. Clean, tidy, obedient data.
By [Author Name]
We are moving toward foundation models for geometry—neural networks that have an intrinsic understanding of the physical world's statistics. The next generation of SVM will not need vanishing points or ground planes. It will simply feel the 3D structure the way a radiologist feels an anomaly in an X-ray.