Paper: Rendering Synthetic Objects into Legacy Photographs

Inserting 3D objects into existing photographs

 

This fascinating video presents a new method to insert 3D objects into existing photographs. It is based on the research of Kevin Karsch, Varsha Hedau, David Forsyth and Derek Hoiem  (all University of Illinois at Urbana-Champaign). Their main contribution is the algorithm, which generates the light model for the scene. The algorithm needs only one photograph and a few manual markings by a novice user together with a ground truth data set to create a near real life insertion. The ground truth data set was generated with 200 images from 20 indoor scenes under varying lighting conditions.

The video is well done and I am surprised whats possible, but I like to see how much user input is really necessary and how well the algorithm and the ground truth perform with other images. What do you think?

More details can be found at Kevin Karsch’s website.

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Reconsidering evaluation data sets

In this blog post I want to share some interessting articles which deal with data sets in computer vision. For starters, in this blog post Tomasz Malisiewicz draws attention to a video lecture by Peter Norvig (Google) in which Mr Norvig showed some interesting results

where algorithms that obtained the best performance on a small dataset no longer did the best when the size of the training set was increased by an order of magnitude. … Also, the mediocre algorithms in the small training size regime often outperformed their more complicated counterparts once more data was utilized.

This is indeed interesting as it is always hard to say how much training and test data is necessary and most scientist, me as well, a far more interested in working on their precious algorithm instead of collecting a solid ground truth. Furthermore, as I pointed out in a comment for Tomasz’ blog post, using 10 times as many pictures would mean, I could only evaluate 3 feature combinations in the time I could have evaluated 30.

Answering to my question on how to handle that trade-off, he advocates nonparametric* approaches and

combining learning with data-driven approaches to reduce test time complexity.

I agree with him, that we definitely should spent more time and effort creating larger groundtruth sets, instead of optimizing our algorithms for a groundtruth that is too small to reveal anything.

For further reading I refer to Prof. Jain’s Blog, where he claims in his blog post, Evaluating Multimedia Algorithms, that the existing data sets for photo retrieval are

too small such as the Corel or Pascal datasets, too specific like the TRECVID dataset, or without ground truth, such as the several recent efforts by MIT and MSRA that gathered millions of Web images for testing,

and promotes his concept for gathering controlled data ground truths.

As the third read the Scienceblog features a story about a James DiCarlo, a neuroscientist in the McGovern Institute for Brain Research at MIT and graduates students Nicolas Pinto and David Cox of the Rowland Harvard Institute who

argue that natural photographic image sets, like the widely used Caltech101 database, have design flaws that enable computers to succeed where they would fail with more authentically varied images. For example, photographers tend to center objects in a frame and to prefer certain views and contexts. The visual system, by contrast, encounters objects in a much broader range of conditions.

They go on

We suspected that the supposedly natural images in current computer vision tests do not really engage the central problem of variability, and that our intuitions about what makes objects hard or easy to recognize are incorrect.”

I think all the three articles remind us, to reconsider the data sets we use for evaluation. Regarding their size,noisiness and their ‘naturality’.

* nonparametric as in using rank or order of the images