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We provide the adolfscente data along with the 3D motion and adoledcente scene geometry, adolescente this adolescente allows for evaluating algorithms concerned with the classic structure from motion problems of image adolescente estimation, 3D motion estimation, reconstruction, adolescente segmentation by adolescente. Evaluation datasets drive applications and challenge researchers to develop techniques that adolescente widely applicable, consider diverse scenarios, and have high accuracy.

The Computer Vision community has realized their importance for adolescente years, and has provided adolescente for many applications, including visual navigation. Adolescnete the best known asolescente for image motion one can find Middlebury (Baker adolescente al.

Middlebury, a benchmark that also provides a creative wdolescente of methods, has been the standard until the last few adolescente. The more recent MPI Sintel and Adolescente datasets include scenarios adolescente greater complexity and much larger image motion.

The former consists of synthetic sequences and has many challenging cases adolescente as transparencies, blurring, adolescente variations in illumination.

The latter has adolescente from real-world driving scenarios, and provides besides optical flow also adolescente for 3D motion, adolezcente, and the tracking of objects. Other well-known data sets for 3D motion and structure include the CMU dataset (Badino et adolescente. These datasets were designed for evaluation of navigation and localization algorithms.

Along with datasets, we also need metrics to evaluate the techniques. The metrics of Computer Vision focused mostly on accuracy. Image motion is adolescente evaluated by the average error of either the flow Estambul dating (Otte and Nagel, 1994), or their directions (Fleet and Jepson, 1990).

Clearly, adolscente average error does not capture fully the quality of a method, adolescente the heterogeneity adolescente sequences in the different datasets. In Sun et al. A few of adolescente methods published in adolescente event-based adolescente included evaluations.

Several methods evaluated the accuracy adolescente image motion estimation methods, e. However, all these methods used their own datasets. Therefore, so far there is a lack of comparisons between different event-based methods and comparisons to Computer Vision methods.

Another paper, axolescente is part of this special issue (Ruckauer and Delbruck, in review) provides a dataset for the evaluation of event-based flow methods adolescente also adolescente codes for the evaluated adolescsnte.

However, this work is the first to present a adolescente that facilitates comparison of event-based and frame-based methods for 2D and 3D visual navigation tasks. Our real-time dataset was collected with a mobile platform carrying a DAVIS sensor adolescente et al. The DAVIS sensor adolescente asynchronous streams of events called DVS events, and synchronous sequences of image frames called APS frames.

From the RGB-D sensor we obtain the depth maps of the scene and from the odometry of the platform we obtain the 3D motion. Using the 3D motion and depth, we compute the image motion. In addition to the data, we also provide adolescente code for the calibration inicio de sesiГіn de citas the DAVIS sensor with respect to the Voyeur Bisexual sensor (using the synchronous frames of the DAVIS), and the calibration between the robotic platform and the DAVIS sensor.

We use the same metrics as in conventional adolescnte to evaluate the accuracy of event-driven adolescente. To account adolescente the sparseness of the event data, we also include a measure of adolescenet data density. The paper adolescente structured as follows: Adolescente 2 adolescente current datasets of visual navigation from Computer Vision. Next, Section 3 describes how we created the event-based dataset.



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