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This paper presents an efcient system for simultaneous dense scene reconstruction and object labeling in real-world environments (captured with an RGB-D sensor). The proposed system starts with the generation of object proposals in the scene. It then tracks spatio-temporally consistent object proposals across multiple frames and produces a dense reconstruction of the scene. In parallel, the proposed system uses an efcient inference algorithm, where object class probabilities are computed at an object-level and fused into a voxel-based prediction hypothesis modeled on the voxels of the reconstructed scene. Our extensive experiments using challenging RGB-D object and scene datasets, and live video streams from Microsoft Kinect show that the proposed system achieved competitive 3D scene reconstruction and object labeling results compared to the state-of-the-art methods.
|Title of host publication||2016 IEEE International Conference on Robotics and Automation (ICRA)|
|Editors||Danica Kragic, Allison Okamura|
|Place of Publication||USA|
|Publisher||IEEE, Institute of Electrical and Electronics Engineers|
|Number of pages||8|
|Publication status||Published - 2016|
|Event||2016 IEEE International Conference on Robotics and Automation: ICRA 2016 - Stockholm, Sweden|
Duration: 16 May 2016 → 21 May 2016
|Conference||2016 IEEE International Conference on Robotics and Automation|
|Period||16/05/16 → 21/05/16|
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