Real-Time Shape Acquisition with Sensor-Specific Precision

Duration: 1. December 2015 - 30. November 2020
Project leader: Michael Wimmer
FWF P24600-N23

General Information

The core idea in this project is to capture the shape of physical objects in real time, with guaranteed precision, and to reconstruct the shape boundaries with minimal geometry. An example application is to let untrained users acquire shapes using emerging mobile sensing devices such as Google’s Project Tango. The user moves the sensor around the object, guided by immediate visual feedback on the input sampling quality. The output is a topologically clean mesh consisting of just the vertices required to represent its features to the desired approximation. The real-time reconstruction enables numerous geometry-processing applications to be taken online, such as shape retrieval/matching, harvesting real-world geometry into a cloud, perspective photo correction, interactive modeling, augmented reality, physics simulation, or fabrication.


9 Publications found:
Image Bib Reference Publication Type
Stefan Ohrhallinger, Michael Wimmer
StretchDenoise: Parametric Curve Reconstruction with Guarantees by Separating Connectivity from Residual Uncertainty of Samples
In Proceedings of Pacific Graphics 2018. August 2018.
[image] [paper] [Extended version] [source]
Conference Paper
Stefan Ohrhallinger, Michael Wimmer
FitConnect: Connecting Noisy 2D Samples by Fitted Neighborhoods
Computer Graphics Forum, (Early view), April 2018. [paper] [Replicability Source Code]
Journal Paper with Conference Talk
Michael Pointner
Multi-Focal Image Generation using Automatic Depth-Based Focus Selection
[image] [thesis]
Bachelor Thesis
Nicolas Grossmann
Extracting Sensor Specific Noise Models
Bachelor Thesis
Thomas Köppel
Extracting Noise Models – considering X / Y and Z Noise
Bachelor Thesis
Mohamed Radwan, Stefan Ohrhallinger, Elmar Eisemann, Michael Wimmer
Cut and Paint: Occlusion-Aware Subset Selection for Surface Processing
In Proceedings of Graphics Interface 2017, pages 82-89. May 2017.
Conference Paper
Siegfried Reinwald
Fast kNN in Screen Space on GPU
[image] [thesis]
Bachelor Thesis
Jeremy Forsythe, Vitaliy Kurlin, Andrew Fitzgibbon
Resolution-independent superpixels based on convex constrained meshes without small angles
Lecture Notes in Computer Science (LNCS), 10072:223-233, December 2016. [paper] [slides]
Journal Paper with Conference Talk
Stefan Ohrhallinger, Scott A. Mitchell, Michael Wimmer
Curve Reconstruction with Many Fewer Samples
Computer Graphics Forum, 35(5):167-176, 2016. [paper] [slides] [Reproducibility Source Code]
Journal Paper with Conference Talk
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