https://github.com/s-gupta/rgbd

Code for CVPR13 paper - Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images
https://github.com/s-gupta/rgbd

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Code for CVPR13 paper - Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images

• Host: GitHub
• URL: https://github.com/s-gupta/rgbd
• Owner: s-gupta
• License: mit
• Created: 2013-10-21T01:35:58.000Z (about 12 years ago)
• Default Branch: master
• Last Pushed: 2021-06-23T22:03:05.000Z (over 4 years ago)
• Last Synced: 2024-09-25T03:54:41.468Z (about 1 year ago)
• Language: MATLAB
• Size: 65.4 MB
• Stars: 82
• Watchers: 12
• Forks: 48
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-edge-detection-papers - NYUDv2 - D Images](https://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Gupta_Perceptual_Organization_and_2013_CVPR_paper.pdf) | CVPR 2013 | Edges come from the boundary of annotated segmentation mask. | (0. Edge detection related dataset)

README

          
This is code to generate the bottom up segmentation, amodal completion,

semantic segmentation and scene classification results for our CVPR 13 paper,

Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images. 

Saurabh Gupta, Pablo Arbelaez, Jitendra Malik, In CVPR 2013

Notes

-----

1. External Dependencies. Please put them in the folder external and modify

startup.m and COM/getPaths.m

  a. gPb-UCM, Contour and region benchmarking code from 

  http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/BSR_full.tgz 

  We modified the files match_segmentations2.m, ucm_mean_pb.cpp, buildW.cpp,

  and the patched files and patches are available in external/ 

  

  b. VLFeat from http://www.vlfeat.org/

  c. liblinear from http://www.csie.ntu.edu.tw/~cjlin/liblinear/, compile this

  to generate train.mex*, predict.mex*

  d. liblinear dense version from

  http://ttic.uchicago.edu/~smaji/projects/digits/, compile this to generate

  trainDense.mex* and predictDense.mex*

  e. SIFT color desprictors from http://www.colordescriptors.com, We used v2.1.

  f. Image Stack Library https://code.google.com/p/imagestack/, for computing

  the local deph boundary cues.

2. The code is intended for use with the data available here. This contains the

color images, preprocessed point clouds and the preprocessed ground truth. 

https://www.dropbox.com/s/qjw05vyvhvzznee/data.tgz?dl=0. Please modify COM/getPaths.m 

and allBenchmarks/benchmarkPaths.m to point to where you put the data folder. 

3. We also provide the 

precomputed results, https://www.dropbox.com/s/kwge19n42k2r9ba/output.tgz?dl=0, 

and the pretrained models, https://www.dropbox.com/s/szcyjrfjhbz4vt8/model.tgz?dl=0.

Data Preprocessing

------------------

1. The data that we provide is different from the one provided by Silberman

etal in the following ways:

  

  a. We use I(46:470, 41:600, :) part of the image. This is different from what

  they use by 1 pixel on each side, I think.

  

  b. We use the original point clouds from the Kinect from over here

  http://horatio.cs.nyu.edu/mit/silberman/nyu_depth_v2/orig_images.mat, and

  project them to the RGB frame but instead of just keeping the Z coordinate

  also keep the X and the Y coordinate (keeping just the Z and projecting back

  rays to get X and Y introduces a little bit of error, but should still work

  well, we did not try using that...)

  c. Finally, we preprocessed the ground truth to get rid of the double

  contours.

Precomputed Results

-------------------

1. https://www.dropbox.com/s/kwge19n42k2r9ba/output.tgz?dl=0:

includes the folder cachedir/release/output which contains the UCM, amodal

completion and the semantic segmentation outputs. Their are also .mat files

with the performance of our system.

Pre trained Models

------------------

1. https://www.dropbox.com/s/szcyjrfjhbz4vt8/model.tgz?dl=0, includes the folder

cachedir/release/model which has the pretrained ucm models, various semantic

segmentation and scene classification models.

Questions and Comments

----------------------

Please feel free to email, sgupta at eecs dot berkeley dot edu for any questions and

comments.