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SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite

SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite. S Song, S. Lichtenberg, and J. Xiao. SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite. Proceedings of 28th IEEE Conference on Computer Vision and Pattern Recognition ( CVPR2015. Video (Download raw video here. SUNRGBD V1 (10.47 G). This file contains the 10335 RGBD images of SUNRGBD V1. 1] N Silberman, D. Hoiem, P. Kohli, R. Fergus. Indoor segmentation and support inference from rgbd images. In ECCV, 2012. Detection.zip (15.1 MB). This work is...

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SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite | rgbd.cs.princeton.edu Reviews
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SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite. S Song, S. Lichtenberg, and J. Xiao. SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite. Proceedings of 28th IEEE Conference on Computer Vision and Pattern Recognition ( CVPR2015. Video (Download raw video here. SUNRGBD V1 (10.47 G). This file contains the 10335 RGBD images of SUNRGBD V1. 1] N Silberman, D. Hoiem, P. Kohli, R. Fergus. Indoor segmentation and support inference from rgbd images. In ECCV, 2012. Detection.zip (15.1 MB). This work is...
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1 abstract
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3 oral presentation
4 data and annotation
5 sunrgbdtoolbox 568 mb
6 evaluation
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9 oral presentation slides
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SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite | rgbd.cs.princeton.edu Reviews

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SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite. S Song, S. Lichtenberg, and J. Xiao. SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite. Proceedings of 28th IEEE Conference on Computer Vision and Pattern Recognition ( CVPR2015. Video (Download raw video here. SUNRGBD V1 (10.47 G). This file contains the 10335 RGBD images of SUNRGBD V1. 1] N Silberman, D. Hoiem, P. Kohli, R. Fergus. Indoor segmentation and support inference from rgbd images. In ECCV, 2012. Detection.zip (15.1 MB). This work is...

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Erik Sudderth

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Erik B. Sudderth, Statistical Computation @ Brown University. I am an Associate Professor. My Learning, Inference, and Vision Group. Develops statistical methods for scalable machine learning, with applications in artificial intelligence, vision, and the natural and social sciences. Particular areas of expertise include:. Graphical models, Bayesian nonparametrics, approximate inference. Object recognition and scene understanding, segmentation, motion and tracking. See my CVPR tutorial. Our work on spectr...

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