Recognition of household objects ( image processing, pattern recognition ) using wide lens?
I share your assumption about depth being useless here.

The approach #1 based on point detectors seems also useless, because there is probably a difference in scale between your reference (learning) images and the representation of objects in the real pictures that is so big that point detection becomes useless. Information is not present at the same level.

So, you're left with approach #2, which sounds reasonable since it's also more-or-less the state-of-the-art in object detection benchmarks.
You may improve the performance of your system by using a training set of pictures that have the size of the objects in the real (test) images.

Also, you will use only the descriptor part of the existing keypoints and apply it in sliding windows. Since oyu have a very poor resolution on objects, I believe that SIFT-like descriptors (e.g., HoG) will have poor performance, while binary descriptors (LBPs, BRISK, FREAK, ORB) will probably behave better (they are less dependant on pixel accurate resolution).

Also, if your objects are very different, you may try a simple image correlation (using again learning images downscaled to the actual size of the objects in the test pictures).