Decades ago, computer vision was primarily thought of as a kind of inverse graphics, the opposite of rendering: image goes in, 3D shapes with material propertie

Decades ago, computer vision was primarily thought of as a kind of inverse graphics, the opposite of rendering: image goes in, 3D shapes with material properties and lighting properties go out. Of course 3D reconstruction is still a huge thing, but object recognition split off onto a different path with the realization that "superficial", 2D-based features (like SIFT and HOG) work very well for recognizing image content, when combined with powerful classifiers and regressors of the time (like the SVM). It was common for lecturers to say that "You may think we need complicated internal 3D representations of everything, but another approach seems more fruitful: ..." Nowadays there's a lot of buzz around merging the two branches back together to unify explicit 3D geometric reasoning and 3D modeling with deep learning. I think humans use both strategies. Sometimes we really rely on superficial visual information, like yellow/black stripes -> time to get away! No need to first perfectly match the visual input to a mental tiger model rotated at the correct orientation. I think split-second recognition is usually like this. Or perhaps we use different strategies for different objects, I could imagine for example that facial recognition in the brain is more 2D-feature based pattern matching, rather than 3D reconstruction.