A Search-Classify Approach for Cluttered Indoor Scene Understanding

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                Joint Seminars
                     by
Dr. Yunhai WANG and Dr. Liangliang NAN

Date:   Monday, 24 September 2012

Venue:  Lecture Theatre F (near lifts 25/26), HKUST

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Speaker:        Liangliang NAN
                Shenzhen Institutes of Advanced Technology (SIAT)
                Chinese Academy of Sciences (CAS)

Title:          "A Search-Classify Approach for Cluttered Indoor Scene
                 Understanding"

Time:           4:30pm - 5:00pm

Abstract:

We present an algorithm for recognition and reconstruction of scanned 3D 
indoor scenes. 3D indoor reconstruction is particularly challenging due to 
object interferences, occlusions and overlapping which yield incomplete 
yet very complex scene arrangements. Since it is hard to assemble scanned 
segments into complete models, traditional methods for object recognition 
and reconstruction would be inefficient. We present a search-classify 
approach which interleaves segmentation and classification in an iterative 
manner. Using a robust classifier we traverse the scene and gradually 
propagate classification information. We reinforce classification by a 
template fitting step which yields a scene reconstruction. We 
deform-to-fit templates to classified objects to resolve classification 
ambiguities. The resulting reconstruction is an approximation which 
captures the general scene arrangement. Our results demonstrate successful 
classification and reconstruction of cluttered indoor scenes, captured in 
just few minutes.


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Biography:

Liangliang Nan is an Associate Professor at the Shenzhen Institutes of
Advanced Technology (SIAT), Chinese Academy of Sciences (CAS). He
received his Ph.D. degree from Shenyang Institute of Automation, Chinese
Academy of Sciences in Feb.2009. His research interests mostly focus on
computer graphics, computer vision and human-computer interaction with an
emphasis on processing and reconstruction from point cloud, detecting
structure semantic information from point cloud, 3D models and images.