2013 Poster Sessions : Guided Real-Time Scanning of Indoor Environments

Student Name : Young Min Kim
Advisor : Leo Guibas
Research Areas: Graphics/HCI
Abstract:
Advances in 3D acquisition devices provide unprecedented opportunities for scanning complex indoor environments. Such raw scans, however, are often noisy, incomplete, and significantly corrupted, making semantic scene understanding difficult, if not impossible. Unfortunately, in most existing workflows, scan quality is assessed after the scanning stage is completed, making it cumbersome to correct for significant missing data by additional scanning. In this work, we present a guided real-time scanning setup, wherein the incoming 3D data stream is continuously analyzed, the data quality automatically assessed. Potentially missing parts are discovered in real-time and highlighted, thus guiding the operator (or the autonomous robot) as ‘where to scan next’. We assess the data quality and completeness of the 3D scan data by comparing against a large collection of commonly occurring indoor man-made objects using an efficient, robust, yet effective scan descriptor. We have tested the system on a large number of simulated and real setups, and found the guided interface to be effective even in cluttered and complex indoor environments.

Bio:
Young Min Kim received the Bachelor of Science in Electrical Engineering from Seoul National University in 2006, and an MS degree in Electrical Engineering from Stanford University in 2008. Since Jan 2007, she has been a Ph.D. candidate at Stanford University. Her research interests include real-time 3D sensors and understanding indoor environment.