The GENUA PESTO Database – GENoa hUman Active fixation database: PEripersonal space STereoscopic images and grOund truth disparity
Creators: name: Gibaldi, Agostino
name: Canessa, Andrea
name: Chessa, Manuela
name: Fato, Marco
name: Solari, Fabio
name: Sabatini, Silvio P.
Publisher: name: F1000Research
Year: 2017

description: The statistics of natural environments is an effective source of information to infer the characteristics of the neural mechanisms underlying the encoding and processing of visual information, from orientation and contrast, to depth and retinal disparity. Specifically for retinal disparity, the available databases usually provide stereoscopic image pairs that are strictly built from computer vision perspective. The cameras are positioned with parallel optical axes, an eye posture seldom assumed by the visual system, and they mainly focus on relatively distant scenes, <i>i.e.</i> where binocular stereopsis is less relevant to depth perception.<br> <br>In the present work, we developed a large dataset of stereoscopic images that is: 1) conceived to mimic the actual posture of the binocular visual system, i.e. considering eye vergence and cyclotorsion, and 2) belongs to the peripersonal space, <i>i.e.</i> where depth perception actually relies on stereopsis. The proposed approach relies on 3D virtual models of natural scenes, characterized by accurate depth information and natural textures. These models have been used in a graphic stereo vision simulator that mimics the natural viewing posture of the human visual system at different gaze directions. The resulting dataset is characterized by high spatial accuracy, realistic color texture, ground-truth disparity maps, occlusions and depth edges. Accordingly, this dataset has a large potential for use in different fields of research.<br><br>In <i>Human Vision</i>, ground-truth data of natural scenes allow for a quantitative characterization of human behaviour, providing a useful means to create fully controlled and accurate visual stimulation for psychophysics and neuroscience experiments. The stereo pairs can be used to investigate the influence of depth cue on:<br>- eye movement,<br>- visual saliency and attention,<br>- surface orientation perception,<br>- object recognition.<br>- retinal disparity statistics, <br>- learn monocular and binocular receptive fields.<br><br>Considering recent widespread of 3D visualization methodologies, from low-cost TV<br>monitors to head-mounted displays, the vergent geometry can be also useful for:<br>- eye-hand coordination,<br>- stereoscopic perception,<br>- image quality assessment.<br><br>In <i>Machine Vision</i>, the ground-truth information included in the dataset, seldom provided in 3D databases, is an optimal instrument to perform unconventional analyses of the problem, in order to develop and benchmark the algorithms themselves. The high accuracy and resolution of the ground-truth disparity data, makes the dataset optimal for an extensive evaluation of disparity estimation. Specifically, since no databases of images with ground-truth vector disparity are available, the proposed database is unique in its kind. Exemplifying, it allows deriving quantitative performance indexes for horizontal and vertical disparity estimation algorithms, both on a pixel and a local basis.<br>Moreover, it can be used for:<br>- image segmentation<br>- object recognition,<br>- surface orientation estimation<br>- occlusion and depth discontinuities estimation,<br>-  visual saliency models.<br>- stereo calibration,<br>- epipolar geometry and camera pose estimation.<br><br>The dataset can be also useful for algorithms not directly related to stereovision, like:<br>- structure from parallax,<br>- three views geometry and trifocal tensor,<br>- multiple view geometry,<br>- feature matching,<br>- affine transformation.