that ReconstructMe adapts itself to changing environments? We’ve put a video online to demonstrate the effects.

This video shows how ReconstructMe handles dynamic environments. It smoothly updates its state as changes occur. This allows us to put ReconstructMe to use where adaptiveness is required. Of course, these changes have to occur in a certain subset of the volume at once. You cannot change the content of the entire volume at once, or tracking failure detection will kick in.

Give it a try!

The following video shows a brand new feature we’ve just added to our main development branch: Automatic Volume Stitching. In this video new volumes are created once the current volume is left. Transformations between volumes are tracked automatically and used to reconstruct a complete surface model. This will allow us to keep the volumes small and the resolution per volume high to create an accurate surface.

Note, that we haven’t applied any post-processing in Meshlab. We might add an Iterative Closest Point algorithm to increase the accuracy at volume seams, but for now, we are pretty happy with the results.

We haven’t decided when to add this feature to the public version, because more testing needs to be done before.

Happy reconstruction,
Christoph

Here are few words on a new feature we’ve added these days to our main development trunk: Tracking failure detection and recovery. Using that feature the system is capable of detecting various tracking failures and recover from that. Tracking failures occur, for example, when the user points the sensor in a direction that is not being covered by the volume currently reconstructed, or when the sensor is accelerated too fast. Once a tracking error is detected, the system switched to a safety position and requires the user to roughly align viewpoints. It automatically continues from the safety position when the viewpoints are aligned.

Here’s a short video demonstrating the feature at work.

We are considering to integrate this feature into the final beta phase, since stability of the system and its usability increase. Be warned, however there are still cases when the systems fails to track and fails to detect that the tracking was lost, causing the reconstruction to become messy.

Our (PROFACTOR) interest in ReconstructMe goes beyond reconstructing 3D models from raw sensor data. The video below shows how to use ReconstructMe to do a stable foreground/background segmentation in 3D. A technique that is often required in 3D vision for pre-processing purposes.

With ReconstructMe you can generate background knowledge by moving the sensor around and thus building a 3D model of the environment. Once you switch from build to track only, ReconstructMe can robustely detect foreground by comparing the environment with the current frame.

This technique can be used for various applications

• Monitoring robotic workcells for human safety aspects.
• Intelligent reconstruction of process relevant objects only. We definitely do a video on this one.

to name a few.

we’ve just launched the BETA phase 2. All participants should have received an E-Mail by now. If not, please leave a reply below. This phase is all about reconstruction from existing data streams.

We wish all participants a happy reconstruction!
Christoph

Hi out there,

we recorded a new video showing the really fast reconstruction of different people in just a few seconds. You also will see the stable camera tracking even although a person walks through the recorded scene. Check out the video!

You can also download the generated model of the last person in the video.

Download as STL model:

{filelink=6}

Original resolution: 75,304 vertices, 2.8MB

Reconstructed-Person by Christoph Kopf is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.