Abstract
We present an attention-based approach for the detection of unknown objects in a 3D environment. The ability to address individual objects in the environment without having previous knowledge about their properties or their identity is one important requirement of the Situated Vision theory. Based on saliency maps, our attention system determines the regions where objects are likely to be found; these are the proto-objects whose extent is refined by a 2D segmentation step. At the same time a 3D scene model is built from measurements of a depth camera. The detected objects are projected into the 3D scene, resulting in 3D object models which are incrementally updated. We show the validity of our approach in an RGB-D sequence recorded in an office environment.
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Notes
This work is part of DFG DACH project FR 2598/5-1 called Situated Vision to Perceive Object Shape and Affordances., in cooperation with TU Wien, RTWH Aachen and IDIAP.
In [7], the TSDF function is raycasted, given a camera pose, to generate a depth map prediction. Using this method in our extended TSDF function means we can generate 2D IOR or object label maps for every new pose of the camera.
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Martín García, G., Frintrop, S. & Cremers, A.B. Attention-Based Detection of Unknown Objects in a Situated Vision Framework. Künstl Intell 27, 267–272 (2013). https://doi.org/10.1007/s13218-013-0256-1
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DOI: https://doi.org/10.1007/s13218-013-0256-1