ACM SIGGRAPH Asia 2008 papers
Cutting-plane training of structural SVMs
Machine Learning
Image-based street-side city modeling
ACM SIGGRAPH Asia 2009 papers
What makes an image memorable?
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
SUN attribute database: Discovering, annotating, and recognizing scene attributes
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Reconstructing the world's museums
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Image memorability and visual inception
SIGGRAPH Asia 2012 Technical Briefs
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An early goal of computer vision was to build a system that could automatically understand a 3D scene just by looking. This requires not only the ability to extract 3D information from image information alone, but also to handle the large variety of different environments that comprise our visual world. This paper summarizes our recent efforts toward these goals. First, we describe the SUN database, which is a collection of annotated images spanning 908 different scene categories. This database allows us to systematically study the space of possible everyday scenes and to establish a benchmark for scene and object recognition. We also explore ways of coping with the variety of viewpoints within these scenes. For this, we have introduced a database of 360° panoramic images for many of the scene categories in the SUN database and have explored viewpoint recognition within the environments. Finally, we describe steps toward a unified 3D parsing of everyday scenes: (i) the ability to localize geometric primitives in images, such as cuboids and cylinders, which often comprise many everyday objects, and (ii) an integrated system to extract the 3D structure of the scene and objects depicted in an image.