Source code for ECCV16 paper
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ann_color
asset_detector/edges
cnn_detection/detection_results
determine_dimensions
download_streetview/draw_floorplan
efficient_render_and_feature
final_visualization
floorplan_visibility
homography
inference
loss_functions
mbs_saliency/precomputed_output
prepare_WebGL
proc_floorplan_label
readme_imgs
render_floorplan
rent_crawler/goodhouses/house74
scoring
segnet_features
readme.md
run.m

readme.md

HouseCraft

Source code for
Hang Chu, Shenlong Wang, Raquel Urtasun, Sanja Fidler. HouseCraft: Building Houses from Rental Ads and Street Views, ECCV 2016. (paper)|(project)|(video)

How to run

No need for external packages or extra configurations. In MATLAB, cd to HouseCraft, and excute
run
Then you should see something like:

step 1: compute all the renders, integral features, and losses  
processing house #74  
Processing time: xx seconds.  
step 2: compute all the final feature  
processing house #74  
Processing time: xx seconds.  
step 3: inference  
processing house #74  
Processing time: xx seconds.  
step 4: visualize  


(You may or may not need to recompile the ann toolbox at HouseCraft/ann_color/ann_wrapper)

Test on the SydneyHouse dataset

Download the SydneyHouse dataset from the HouseCraft project page.
Unzip the downloaded zip file to the HouseCraft folder.
In HouseCraft/run.m, modify line 7, 28, 45, 60 to for ii=1:length(houseidlist).
Excute run.

Notes

  • There are nearly a hundred functions in this project. I tried my best (as a graduate student) to comment all of them as much as I could.
  • I also included visualization option in many functions. For example, in many individual functions, by setting a variable (typically named display) as 1, you will see things like: