v2 and v1 of this dataset had no info on region centers. The habitat version of the original matterport data does

[rishiswethan]

Thanks for your excellent work.
Due to the lack of center coordinates, I am not able to extract the images from all regions of a scene for semantic segmentation. When I use the habitat version of the original matterport dataset that is downloaded from download_mp.py, it has the centers of regions. Is there any other way to get centers, or to get all regions somehow without the center?

I tried doing things like using the coords door, ceiling, etc to find a center, but all were bad, as I couldn't point to which direction it should move, in order to reach the center. I think a workaround would be to detect all walls and ceilings, then compute the centers that way, but I doubt this will work for all images. Is there any other workaround?

This is the result from minival/00800-TEEsavR23oF and hm3d_annotated_minival_basis.scene_dataset_config.json

House has 0 levels, 14 regions and 661 objects
House center:[0. 0. 0.] dims:[-inf -inf -inf]
Region id:_-1, category:None, center:[0. 0. 0.], dims:[-inf -inf -inf]
Region id:_1, category:None, center:[0. 0. 0.], dims:[-inf -inf -inf]
Region id:_2, category:None, center:[0. 0. 0.], dims:[-inf -inf -inf]
Region id:_3, category:None, center:[0. 0. 0.], dims:[-inf -inf -inf]
Region id:_4, category:None, center:[0. 0. 0.], dims:[-inf -inf -inf]
Region id:_5, category:None, center:[0. 0. 0.], dims:[-inf -inf -inf]
Region id:_6, category:None, center:[0. 0. 0.], dims:[-inf -inf -inf]
Region id:_7, category:None, center:[0. 0. 0.], dims:[-inf -inf -inf]
Region id:_8, category:None, center:[0. 0. 0.], dims:[-inf -inf -inf]
Region id:_9, category:None, center:[0. 0. 0.], dims:[-inf -inf -inf]
Region id:_10, category:None, center:[0. 0. 0.], dims:[-inf -inf -inf]
Region id:_11, category:None, center:[0. 0. 0.], dims:[-inf -inf -inf]
Region id:_12, category:None, center:[0. 0. 0.], dims:[-inf -inf -inf]
Region id:_13, category:None, center:[0. 0. 0.], dims:[-inf -inf -inf]

This is from mp3d/1LXtFkjw3qL and mp3d.scene_dataset_config.json

Region id:2_0, category:bedroom, center:[-0.696105 5.876904 7.47785 ], dims:[5.82755 5.0632906 4.642979 ]
Region id:2_1, category:bedroom, center:[-0.73821497 5.869445 13.285263 ], dims:[5.69943 5.0556297 6.9206686]
Region id:2_2, category:bathroom, center:[-1.5556049 5.855009 16.014853 ], dims:[3.92283 4.8258 2.7374992]
Region id:2_3, category:toilet, center:[-0.1834985 4.628419 15.450899 ], dims:[1.273465 2.3871403 1.513999 ]
Region id:2_4, category:spa/sauna, center:[-1.038781 4.380009 4.0081596], dims:[3.648958 1.9890797 2.3295202]
Region id:1_5, category:porch/terrace/deck, center:[-5.63754 1.6281538 11.358154 ], dims:[ 3.8106003 3.0849504 11.70929 ]
Region id:1_6, category:kitchen, center:[-1.6988602 4.5161204 -0.6340549], dims:[7.36716 7.813819 7.249289]
Region id:1_7, category:hallway, center:[-4.595725 1.6373835 4.9533896], dims:[1.73205 2.9854317 3.9453 ]
Region id:1_8, category:stairs, center:[1.414272 3.6792169 2.6212902], dims:[1.354676 5.7876062 5.110919 ]
Region id:1_9, category:stairs, center:[-4.5807953 1.6682456 3.214875 ], dims:[1.8436699 2.884709 0.5157094]
Region id:1_10, category:office, center:[-0.82038 1.656003 15.7973 ], dims:[5.5746 3.0550919 2.8691978]
Region id:1_11, category:living room, center:[-0.803185 1.6077166 9.955522 ], dims:[5.45431 2.960826 8.798361]
Region id:1_12, category:hallway, center:[1.9568756 2.5135574 6.4436903], dims:[4.127609 5.003624 9.56222 ]
Region id:0_13, category:closet, center:[ 0.539799 -1.6975722 4.8729296], dims:[0.6300919 2.7176151 0.72369957]
Region id:1_14, category:hallway, center:[ 3.03258 1.5403118 14.23005 ], dims:[1.9445 3.0837345 6.0061016]
Region id:0_15, category:bathroom, center:[ 0.5301975 -1.68649 14.789148 ], dims:[2.610045 2.730423 4.872699]
Region id:0_16, category:bedroom, center:[-4.1186237 -1.6176748 15.797 ], dims:[6.673073 2.6894484 2.7996006]
Region id:0_17, category:bedroom, center:[-4.6803603 -1.6154566 12.89305 ], dims:[5.579221 2.685926 2.9934998]
Region id:0_18, category:bedroom, center:[-4.6226 -1.61621 9.868441], dims:[5.5316 2.6871195 3.0303192]
Region id:0_19, category:bedroom, center:[-4.574565 -1.6151962 6.9728546], dims:[5.44477 2.6855097 2.7280297]
Region id:0_20, category:hallway, center:[-0.46183947 -1.6429033 8.91875 ], dims:[ 2.5349011 2.6403122 10.9487 ]
Region id:0_21, category:toilet, center:[ 1.0019575 -1.6828442 5.910165 ], dims:[1.413585 2.723012 1.0809898]
Region id:0_22, category:closet, center:[ 0.884981 -1.6886387 8.2303705], dims:[1.2529579 2.7319617 2.2224197]
Region id:0_23, category:closet, center:[ 0.895914 -1.6896095 11.102665 ], dims:[1.311772 2.7334614 2.2736702]
Region id:0_24, category:stairs, center:[-0.43271753 -2.6956449 3.1824796 ], dims:[2.453765 0.36514997 0.60734034]
Region id:0_25, category:hallway, center:[ 0.34897995 -1.0808079 0.7692351 ], dims:[3.0130801 2.8645244 4.5243893]
Region id:0_26, category:bathroom, center:[ 1.0604529 -1.028461 -2.856915 ], dims:[1.6081738 2.7615576 2.7168703]
Region id:1_27, category:porch/terrace/deck, center:[ 3.139145 2.605465 -1.3372247], dims:[1.8337297 5.35499 6.0002294]
Region id:0_28, category:stairs, center:[ 1.280018 -1.1482306 1.670922 ], dims:[1.015024 2.3518791 2.130136 ]
Region id:0_29, category:workout/gym/exercise, center:[-3.04459 -1.0788939 0.6934099], dims:[4.3276005 2.8613925 4.1135197]
Region id:0_30, category:bedroom, center:[-2.4535055 -1.0308 -2.7611852], dims:[5.3881693 2.7652996 2.7803698]