3D Wikipedia source code

Here you will find the Matlab and Python source code for automatically labeling objects in 3D models given a reference text, such as Wikipedia.

CITATION:

Bryan C. Russell, Ricardo Martin-Brualla, Daniel J. Butler, Steven M. Seitz, and Luke Zettlemoyer. 3D Wikipedia: Using Online Text to Automatically Label and Navigate Reconstructed Geometry. ACM Transactions on Graphics (SIGGRAPH Asia 2013), Vol. 32, No. 6. PDF | BibTeX | Project page

DOWNLOAD:

You can download a zip file of the source code directly.

Alternatively, you can clone it from GitHub as follows:

$ git clone https://github.com/brussell123/3dwikipedia.git

INSTALLATION:

1. For the results in the paper we used the Stanford parser. You can download the version we used here:

   http://nlp.stanford.edu/software/stanford-parser-2012-07-09.tgz

   Uncompress the tarball and insert into "./code/LIBS/".

   We found it helpful to raise the memory limit. You can set the memory limit inside "stanford-parser-2012-07-09/lexparser.sh" by changing "-mx1000m" (we set it to be "-mx4000m").

2. To download images from Google Image Search, be sure to set your user IP address inside the following file before running the scripts:

   ./code/LIBS/google_image_search/userip.txt

   See the following as a reference for the Google Image Search API:

   https://developers.google.com/console/help/#activatingapis

3. Download the Bundler tarball:

   http://www.cs.cornell.edu/~snavely/bundler/distr/bundler-v0.4-source.tar.gz

   Uncompress the tarball and insert into "./code/LIBS/".

4. In Matlab, run "compile" to compile all binaries.

RUNNING THE CODE:

1. You can run the code demo for the Pantheon example by downloading its text, reference panorama, and pre-computed "bundle" struct. Uncompress the tarball and insert into the "./data" directory. For this example you may skip to step 2 below.

   To run on your own data, start by building a 3D model of the site. You will need to download images to build the 3D model, e.g. by querying Flickr for the site name and downloading images through their API. A couple of possibilities for building a sparse 3D point cloud is via VisualSFM (recommended):

   http://ccwu.me/vsfm/

   or Bundler:

   http://www.cs.cornell.edu/~snavely/bundler/

   We provide Matlab scripts to read the sparse point cloud. To read the output from VisualSFM (e.g. "pantheon.nvm"), run the following in Matlab:

   >> addpath ./code;
   >> bundle = ReadNVMFile('pantheon.nvm');

   To read the output from Bundler (e.g. the output lives in a directory "/path/to/pantheon"), run the following in Matlab:

   >> addpath ./code;
   >> bundle = readBundleFile('/path/to/pantheon');

2. Given the text, reference image, and "bundle" struct, adjust the global variables at the top of the demoDetect.m script and run in Matlab.

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Copyright (C) 2014 Intel, University of Washington, Bryan C. Russell, Ricardo Martin-Brualla, Daniel J. Butler, Steven M. Seitz, Luke Zettlemoyer