What makes object representation and recognition hard?_问答_开发者

Intuitively, it would seem that given a dozen or so 2d images from different angles of almost any object, it should be easy to construct a 3d representation of that object. Subsequently a library of 3d representations attained in this way co开发者_开发技巧uld be used to identify new 2d images.

What literature is there along these lines, and why has it not yet produced strong object recognition?

It is your word "intuitively" that is causing you trouble there. Your brain is not designed to be very good at certain tasks, like multiplying thousands of numbers in an instant. However for raw computational power your brain makes the fastest computer look like mere tiddly-winks (neural response time of only about 10 milliseconds, but all those 10^14 or so neurons all working in parallel totally beats any modern machine). Its just that your brain is designed to solve problems that are intensely more computationally complex, like recognizing objects in a picture, parsing sound data and picking out individual speakers amidst background noise. Learning to classify and deal with tens of thousands of types of objects.

The incredibly computationally intense things your brain is designed to do really well are the things that, to a person, seem "intuitive". The things it isn't designed to do really well seem "unintuitive" or difficult. But the raw computation needed for strong object recognition (because there are just so MANY kinds of objects, many of which really have subobjects, and multiple classifications, and non-rigid forms, e.g. "trousers", "water", "dog") is WAY more than what is needed accomplish things one considers only possible for a computer. Things like using "common sense" to solve an every day problem are similarly trivial for a person, but computationally incredibly complex.

What you want to do is indeed possible, but (there are quite a few buts)

for the 3D reconstruction:

For anything but the simplest shapes you need more than just a few dozen images.
The shape you are reconstructing needs to have a lot of recognizable features that look similar enough from different angles so that you can match them.
Lighting needs to be fairly constant over your entire set of images, otherwise shadows will throw you off (or you need even more images)
even with very feature rich objects (i.e. lot of variation in colour and shape) 3D reconstruction accuracy from any matched pair of features is going to be terrible if you do not have full knowledge of the parameters (position, view direction and opening angle) of the camera used to take each picture.

These are all problems can be solved, so suppose you did, and now you have a new picture from the object that you want to match to your 3D shape.

You could of course try to find a 2D projection of your shape that fit the new picture, but the search space there is enormous. It would probably be a lot easier and faster to use the feature finding and matching system you built for the initial 3D reconstruction to directly match the new picture to the existing set, and find where it fits on the object that way.

So once you've solved the problem of creating the initial 3D reconstruction your second step is basically done as well.

Photosynth is a brilliant example of these two steps. Browse the site, try to find some of the references they have there.

As for your final step, strong object recognition, just imagine the search space! What you need for strong object recognition, apart from a good representation of the objects you want to recognize, is a good way to search the space of objects you know, and a good way to represent your new object (the image of an object in this case) in that space. This is something I know nearly nothing about.

For just matching the same object in different 2D images there are SIFT features. But I don't think this translates well to 3D.

Note that what you're describing is instance recognition. Computer can indeed do a good job of instance recognition these days. For example, Google Goggles is very good at recognizing landmarks like the Golden Gate Bridge and Eiffel Tower.

However, computers are less good at doing category recognition and classification. Creating dozens of 2D snapshots for all possible objects under all types of lighting conditions etc. becomes intractable very quickly. The fact that certain objects such as a dog can move around makes the space of possibilities even bigger. Computers become much worse at this.

Also, from the biological standpoint, our visual field is around 100 million pixels. Graphics cards have only now started to become capable of rendering that much data in real-time. Making sense of that much data is even more computationally intensive.

One often talks about having a machine reach a 5 year old's ability to process information. But let's think about how much data that is. 100 million pixels with 3 color channels and 1 byte per pixel = 300MB/s. Now multiply that by 30 frames per second, 31,556,926 seconds per year, and 5 years, you end up with roughly 1.4 exabytes (1.4x10^18).