Object disappearance for object discovery

By Julian Mason, Bhaskara Marthi, Ronald E. Parr. 2012

24 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “7 Sample images from the large dataset [60].”
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes (2017, background): “A large number of RGB-D datasets have been captured and made publicly available for training and benchmarking [56, 34, 50, 65, 79, 83, 74, 4, 58, 81, 15, 55, 1, 68, 30, 51, 21, 48, 43, 92, 80, 61, 72, 93, 36, 16, 35, 57, 40, 29, 70, 52, 45, 95, 75, 9, 33, 85, 71, 32, 3, 10, 78, 2].”
Unsupervised discovery of object classes with a mobile robot (2014, unknown): “[14] do so with RGB-D data.”
SUN RGB-D: A RGB-D scene understanding benchmark suite (2015, unknown): “There are many existing RGB-D datasets available [54] [47] [1] [25] [44] [60] [49] [45] [29] [66] [57] [52] [46] [16] [21] [73] [35] [67] [3] [41] [10] [63] [42] [64] [65] [48] [12] [33] [8] [50] [26] [6].”
You-Do, I-Learn: Egocentric unsupervised discovery of objects and their modes of interaction towards video-based guidance (2015, methodology): “In [37], the disappearance of features in a position is used as a cue to discover objects from RGB-D images collected using a mobile platform.”
RGBD Datasets: Past, Present and Future (2016, background): “mes total from 4 Kinects ## Polygon segmentation of objects arranged on kitchen worktop ’12 Willow garage dataset [2] ~160 frames ## Dense pixel labeling ’12 Object Disappearance for Object Discovery [73] 1231 frames X # Ground truth object segmentations of objects of interest ’12 NYUv2 [93] 1449 fr”