Welcome to WARM 2009, where augmented reality eggheads from both sides of the Danube meet for 2 days to share ideas and collaborate.
It’s the 4th year WARM is taking place – always in Graz university, and always in February – to provide an excuse for a skiing event, once the big ideas are taken in. Hence the cunning logo:
This year 54 attendees from 16 different organizations in 5 countries are expected (Austria, Germany Switzerland, England and the US). The agenda is jam-packed with XX sessions, Lab demos and a keynote by Oliver Bimber. I have the unenviable pleasure of speaking last.
It’s 10 am. Lights are off. Spotlight on Dieter Schmalstieg, the master host, taking the stage to welcome everybody.
He admits, the event started as a Graz meeting and just happened because guests kept coming.
Daniel Wagner, the eternal master of ceremony of WARM, introduces Simon Hay from Cambridge (Tom Drummond group) the first speaker in the Computer Vision session. Simon will talk about “Repeatability experiments for interest point location and orientation assignment” – an improvement in feature based matching for the rest of us…
The basic idea: detect interest regions in canonical parameters.
Use, known parameters that come through Ferns, PhonySift, Sit Mops, and MSERs searches,
and accelerate and improve the search with location detectors and orientation assignments.
After a very convincing set of graphs, Simon concludes by confirming Harris and FAST give reasonable performance and gradient orientation assignment works better than expected.
Next talk is by Qi Pan (from the same Cambridge group) about “Real time interactive 3D reconstruction.”
From the abstract:
“High quality 3D reconstruction algorithms currently require an input sequence of images or video which is then processed offline for a lengthy time. After the process is complete, the reconstruction is viewed by the user to confirm the algorithm has modelled the input sequence successfully. Often certain parts of the reconstructed model may be inaccurate or sections may be missing due to insufficient coverage or occlusion in the input sequence. In these cases, a new input sequence needs to be obtained and the whole process repeated.
The aim of the project is to produce a real-time modelling system using the key frame approach which provides immediate feedback about the quality of the input sequence. This enables the system to guide the user to provide additional views for reconstruction, yielding a complete model without having to collect a new input sequence.”
Couldn’t resist pointing out the psychological sounding algorithms (and my ignorance) Qi uses such as Epipolar Geometry and PROSAC, reconstructing Delauney Triangulation followed by probabilistic Tetrahedral carving. You got to love these terms.
The result is pretty good, though still noisy – so stay tuned for future results of Qi’s research.
Third talk is by Vincent Lepetit from Computer Vision Lab from the Swiss CV Lab at EPFL.
Vincent starts with a recap of Keypoint recognition: Train the system to recognize keypoints of an object.
Vincent then demonstrates works leveraging this technique: an awarded work by Camille Scherrer “Le monde des montagnes” a beautiful augmented book, and a demo by Total Immersion targeted for advertising.
Now, on to the new research dubbed Generic Trees. The motivation is to speed up the training phase and to scale.
A comparison results shows it’s 35% faster. To prove, he shows a video of a SLAM application.
Generic Trees method is used by Willow Garages for autonomous robotics – which is implementing Open CV.
Next, he shows recognizing camera pose with 6 degrees of freedom (DOF) based on a single feature point (selected by the user). Impressive.
That’s a wrap of the brainy Computer Vision session. Next is Oliver Bimber’s keynote.