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Sensors used for 3D-reconstruction determine both the quality of the results and the nature of reconstruction algorithms. The spectrum of such sensors ranges from expensive to low cost, from highly specialized to outof-the-shelf, and from stereo to mono sensors. The list of available sensors has been growing steadily and is becoming difficult to manage, even in the consumer sector. We provide a survey of existing consumer 3D sensors and a taxonomy for their assessment. This taxonomy provides information about recent developments, application domains and functional criteria. The focus of this survey is on low cost 3D sensors at an accessible price. Prototypes developed in academia are also very interesting, but the price of such sensors can not easily be estimated. We try to provide an unbiased basis for decision-making for specific 3D sensors. In addition to the assessment of existing technologies, we provide a list of preferable features for 3D reconstruction sensors. We close with a discussion of common problems in available sensor systems and discuss common fields of application, as well as areas which could benefit from the application of such sensors.

In the last decades, a large diversity of automatic, semi-automatic and manual approaches for video segmentation and knowledge extraction from video-data has been proposed. Due to the high complexity in both the spatial and temporal domain, it continues to be a challenging research area. In order to develop, train, and evaluate new algorithms, ground truth of video-data is crucial. Pixel-wise annotation of ground truth is usually time-consuming, does not contain semantic relations between objects and uses only simple geometric primitives. We provide a brief review of related tools for video annotation, and introduce our novel interactive and semi-automatic segmentation tool iSeg. Extending an earlier implementation, we improved iSeg with a semantic time line, multithreading and the use of ORB features. A performance evaluation of iSeg on four data sets is presented. Finally, we discuss possible opportunities and applications of semantic polygon-shaped video annotation, such as 3D reconstruction and video inpainting.

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