So, quick story while you finish your lunch.

For my M.Tech thesis I've been working on a model that looks at one ordinary camera image and does two jobs at once:

Finds the objects : cars, pedestrians, cyclists (object detection).

Guesses how far away everything is : a distance value for every single pixel (monocular depth estimation).

Think of a dashcam that can say "there's a car, and it's about 14 metres ahead." That's the dream.

The paper just got accepted at ICTCS 2026 (Springer LNNS), so I figured I'd tell you what we actually found without all the equations.

The problem: the model was studying the wrong chapter

Here's the thing nobody tells you about depth models.

When you train one, the loss function checks the predicted distance at every pixel and treats them all equally. Sounds fair, right?

Now look at a typical driving photo. Most of it is road, sky, buildings and trees .…