The Southampton-York Natural Scenes (SYNS) dataset: statistics of surface attitude

Recovering 3D scenes from 2D images is an under-constrained task; optimal estimation depends upon knowledge of the underlying scene statistics. Here we introduce the Southampton-York Natural Scenes dataset (SYNS: https://syns.soton.ac.uk), which provides comprehensive scene statistics useful for understanding biological vision and for improving machine vision systems. In order to capture the diversity of environments that humans encounter, scenes were surveyed at random locations within 25 indoor and outdoor categories. Each survey includes (i) spherical LiDAR range data (ii) high-dynamic range spherical imagery and (iii) a panorama of stereo image pairs. We envisage many uses for the dataset and present one example: an analysis of surface attitude statistics, conditioned on scene category and viewing elevation. Surface normals were estimated using a novel adaptive scale selection algorithm. Across categories, surface attitude below the horizon is dominated by the ground plane (0° tilt). Near the horizon, probability density is elevated at 90°/270° tilt due to vertical surfaces (trees, walls). Above the horizon, probability density is elevated near 0° slant due to overhead structure such as ceilings and leaf canopies. These structural regularities represent potentially useful prior assumptions for human and machine observers, and may predict human biases in perceived surface attitude

The Southampton York natural scenes (SYNS) dataset

Humans are adept at estimating 3D scene geometry from a stereo image pair, or even from a single image. Computer vision algorithms are less good. Gaining traction on this problem requires a dataset that contains good quality images and ground truth data, and represents the complex and diverse scenes that we encounter. To this end we have developed the Southampton York Natural Scenes (SYNS) public dataset: https://syns.soton.ac.uk