Learning 3D Scene Synthesis from Annotated RGB-D Images
We present a data‐driven method for synthesizing 3D indoor scenes by inserting objects progressively into an initial, possibly, empty scene. Instead of relying on few hundreds of hand‐crafted 3D scenes, we take advantage of existing large‐scale annotated RGB‐D datasets, in particular, the SUN RGB‐D...
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| Vydáno v: | Computer graphics forum Ročník 35; číslo 5; s. 197 - 206 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Oxford
Blackwell Publishing Ltd
01.08.2016
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | We present a data‐driven method for synthesizing 3D indoor scenes by inserting objects progressively into an initial, possibly, empty scene. Instead of relying on few hundreds of hand‐crafted 3D scenes, we take advantage of existing large‐scale annotated RGB‐D datasets, in particular, the SUN RGB‐D database consisting of 10,000+ depth images of real scenes, to form the prior knowledge for our synthesis task. Our object insertion scheme follows a co‐occurrence model and an arrangement model, both learned from the SUN dataset. The former elects a highly probable combination of object categories along with the number of instances per category while a plausible placement is defined by the latter model. Compared to previous works on probabilistic learning for object placement, we make two contributions. First, we learn various classes of higher‐order object‐object relations including symmetry, distinct orientation, and proximity from the database. These relations effectively enable considering objects in semantically formed groups rather than by individuals. Second, while our algorithm inserts objects one at a time, it attains holistic plausibility of the whole current scene while offering controllability through progressive synthesis. We conducted several user studies to compare our scene synthesis performance to results obtained by manual synthesis, state‐of‐the‐art object placement schemes, and variations of parameter settings for the arrangement model. |
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| AbstractList | We present a data-driven method for synthesizing 3D indoor scenes by inserting objects progressively into an initial, possibly, empty scene. Instead of relying on few hundreds of hand-crafted 3D scenes, we take advantage of existing large-scale annotated RGB-D datasets, in particular, the SUN RGB-D database consisting of 10,000+ depth images of real scenes, to form the prior knowledge for our synthesis task. Our object insertion scheme follows a co-occurrence model and an arrangement model, both learned from the SUN dataset. The former elects a highly probable combination of object categories along with the number of instances per category while a plausible placement is defined by the latter model. Compared to previous works on probabilistic learning for object placement, we make two contributions. First, we learn various classes of higher-order object-object relations including symmetry, distinct orientation, and proximity from the database. These relations effectively enable considering objects in semantically formed groups rather than by individuals. Second, while our algorithm inserts objects one at a time, it attains holistic plausibility of the whole current scene while offering controllability through progressive synthesis. We conducted several user studies to compare our scene synthesis performance to results obtained by manual synthesis, state-of-the-art object placement schemes, and variations of parameter settings for the arrangement model. |
| Author | Kermani, Z. Sadeghipour Zhang, H. Liao, Z. Tan, P. |
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| Cites_doi | 10.1145/2366145.2366154 10.1109/CVPR.2015.7298655 10.1145/2010324.1964982 10.1109/TPAMI.2009.145 10.1007/978-1-4471-4640-7_8 10.1145/2591010 10.1109/ICCV.2013.458 10.1145/2661229.2661288 10.1145/2010324.1964981 10.1145/2366145.2366157 10.1145/2461912.2461968 10.1145/2766933 10.1145/2601097.2601109 10.1007/978-3-319-10602-1_48 10.1145/2661229.2661239 10.1145/2816795.2818057 10.1016/0377-0427(87)90125-7 10.1007/978-3-642-33715-4_54 |
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| References_xml | – reference: Hu R., Zhu C., van Kaick O., Liu L., Shamir A., Zhang H.: Interaction context (icon): Towards a geometric functionality descriptor. ACM Transactions on Graphics (Special Issue of SIGGRAPH) 34, 4 (2015), Article 83. 5 – reference: Zhang Y., Song S., Tan P., Xiao J.: PanoContext: A whole-room 3D context model for panoramic scene understanding. In Proc. Euro. Conf. on Computer Vision (2014), vol. 6, pp. 668-686. 1 – reference: Kim Y.M., Mitra N.J., Yan D.-M., Guibas L.: Acquiring 3d indoor environments with variability and repetition. ACM Trans, on Graphics 31, 6 (2012), 138: 1-138:11. 1 – reference: Yu L.-F., Yeung S.K., Tang C.-K., Terzopoulos D., Chan T.F., Osher S.: Make it home: automatic optimization of furniture arrangement. ACM Trans. on Graphics (Proc. of SIGGRAPH) 30, 4 (2011), 86: 1-12. 1, 2 – reference: Fisher M., Ritchie D., Savva M., Funkhouser T., Hanrahan P.: Example-based synthesis of 3d object arrangements. 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| Title | Learning 3D Scene Synthesis from Annotated RGB-D Images |
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