Data-driven Depth Inference from a Single Still Image  Project Goal The purpose of this project is to use a lot of training data to obtain a more data-driven approach for recovering depth information given a single image.  Data The indoor images with 4 scene categories i.e., office, kitchen, bedroom, and living room collected with Microsoft Kinect RGB camera and depth camera ‘office’ ‘kitchen’ ‘bedroom’ ‘living room’ Kyunghee Kim, Computer Science Department, Stanford University 1

Data-driven Depth Inference from a Single Still Image  Control Experiments depth inference from training images 1) in all scene categories 2) in the same scene categories 3) in different scene categories vary path size : 3 by 3, 5 by 5, 9 by 9, 11 by 11 vary the number of training patches : 1000, 2000, 3000 Number of training patches : 1000 Number of training patches : 3000  2nd Control Experiments : in the same scene categories  Future work - SIFT hierarchical clustering pairwise term 3D technology Patch size Number of training patches Kyunghee Kim, Computer Science Department, Stanford University 2