1. 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

2. 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