What's the best way to create a 3D model from the "average" shape of real-world objects?

I don't know if this works for you, but the approach that comes to my mind is to average the images first, and then create the model from the blended image. So load up 25* photos of bananas or whatever in to your favorite image manipulation program, and tweak away. When you have a good "average" representation, then create the 3D model. Of course this won't work if the point of the project is to average various 3D models, but if the objective is to have a representative (if not perfect) model, it should work pretty well. *simplified for readability, you'd probably need 4-6 photos per object, so if it was 10 objects, you'd need 40-60 calibrated photos.

In the paper by Xu et al., they assume that a correspondence map has been constructed prior to their technique being applied. They give some references, but they are too complicated for me to understand with a cursory glance. You could try to read those or talk to an expert in surface correspondence (surely around every corner).

Wood's link is, in fact, incredible, especially if, as it says in figure 5, the models aren't aligned and can be rotated, and the topologies are different (which it doesn't say), how does it know that an ear becomes an ear and not an eye?

signal, the topology problems you describe are exactly what I would expect, but did you see http://www.cad.zju.edu.cn/home/xudong/Projects/mesh_morphing/main.htm? If that's completely automated it seems incredible. (So I'm wondering what the phrase "with proper boundary conditions," means exactly.)

One problem you're going to run into with morphing the 3d models, is that (at least in the apps I use) both 'ends' of the morph need to have the exact same topology for the morph to work. This could be a pain using any 'generated' 3d models, as their topology would probably vary from model to model. I'd define a standard set of feature points to mark on each 3d object (if it was a human: the tip of the nose, the left elbow, etc.), then use a 3d arm-digitizer, import the point cloud into Maya and then average the positions for each corresponding feature of points (using one of the features as the reference for the rest) using MEL or (preferably) PyMEL, then create a mesh from that. If you don't have a digitizer, you can take a set of photographs and use that, though it's more of a pain.

If you have matched models, you could try using PCA? That's what we use in 2D to model variation in shape (and therefore averages, after a fashion). So, in the 2d case, say you use 15 points to define a banana, and you can then align these points (so point1 on model1 corresponds to point2 on model2), then you can do statistics on the matched points. If you do modelising, alignment, then PCA, it's called a http://en.wikipedia.org/wiki/Point_Distribution_Model (invented by Cootes and Taylor). Another thing you might want to google is ASM (active shape model).

itchi23, here's the first hit on google for "shape interpolation", it shows three heads being "averaged". http://www.cad.zju.edu.cn/home/xudong/Projects/mesh_morphing/main.htm This is a huge research area (many siggraph papers etc) & there is no "correct" answer just a bunch of different answers to different questions. Fun stuff to read though and the papers all generally have neat pictures. If you need a 3D model then averaging the images and then trying to produce a 3D model is not the first thing I'd try. Generally image-based morphing techniques are in no way designed to produce a set of different images that still describe a consistent 3D object. Without more specificity about your problem there exists a really large range of solutions.

Even with a 3D scan, you would have to register the datasets together. This is actually sounding a bit similar to a project I've talked about. Say you had 10 CT scans of different people's right femur. You would need some kind of deformable 3D registration technique to correlate the data from each of the scans before you even try to average it correctly. This kind of thing is still an open research problem. I'm not an expert in this field, but you can take a look at http://www.ri.cmu.edu/pubs/pub_3162.html for voxel based stuff or you could go for geometrical correspondence: search for "3D Shape correspondence" on Google. Good luck.

Hmmm - all of these are really good points. The banana was a too simple example. I suppose I can say the objects are complex shapes - let's say a specific animal part taken from a variety of samples. For example - let's take a human's right femur. Obviously there'd be a variance in sizes, structure, but overall retaining a similar shape and scale throughout all the samples. For the purposes of this project, all abnormal physicality (in the femur example, stuff like bone spurs, fracture scarring, etc.) would be eliminated from the sample pool. A 3D scan is a possibility, but might not work for practical reasons surrounding this project. Any additional suggestions are welcomed.