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Scene understanding is an important and fundamental problem in computer vision and is critical in applications of robotics and augmented reality. Scene understanding includes many tasks such as scene labeling, object recognition, and scene classification. Most previous scene understanding methods focus on outdoor scenes. In contrast, indoor scene understanding is more challenging, due to poor illumination and cluttered objects. With the wide availability of affordable RGB-D cameras such as Kinect, huge changes have been made to indoor scene analysis due to the rich 3D geometry information provided by depth measurements.

Wang Anran is a PhD candidate at the School of Computer Engineering of Nanyang Technological University, Singapore. She received her B.E. degree in Computer Science and Technology from Tianjin University, China, in 2012. Her research interests include computer vision and machine learning, specifically, RGB-D scene understanding. She has published several papers in top referred journals and conferences including ICCV, ECCV & TIP. She is a recipient of the Top 10% Best Paper Award from IEEE International Workshop on Multimedia Signal Processing in 2014.