Booster Dataset

Zama Ramirez, Pierluigi ; Tosi, Fabio ; Poggi, Matteo ; Salti, Samuele ; Mattoccia, Stefano ; Di Stefano, Luigi (2022) Booster Dataset. University of Bologna. DOI 10.6092/unibo/amsacta/6876. [Dataset]
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We present a novel high-resolution and challenging stereo dataset framing indoor scenes annotated with dense and accurate ground-truth disparities. Peculiar to our dataset is the presence of several specular and transparent surfaces, i.e. the main causes of failures for state-of-the-art stereo networks. Our acquisition pipeline leverages a novel deep space-time stereo framework which allows for easy and accurate labeling with sub-pixel precision. We release a total of 419 samples collected in 64 different scenes and annotated with dense ground-truth disparities. Each sample include a high-resolution pair (12 Mpx) as well as an unbalanced pair (Left: 12 Mpx, Right: 1.1 Mpx). Additionally, we provide manually annotated material segmentation masks and 15K unlabeled samples. We evaluate state-of-the-art deep networks based on our dataset, highlighting their limitations in addressing the open challenges in stereo and drawing hints for future research.

Document type
Zama Ramirez, PierluigiUniversity of Bologna0000-0001-7734-5064
Tosi, FabioUniversity of Bologna0000-0002-6276-5282
Poggi, MatteoUniversity of Bologna0000-0002-3337-2236
Salti, SamueleUniversity of Bologna0000-0001-5609-426X
Mattoccia, StefanoUniversity of Bologna0000-0002-3681-7704
Di Stefano, LuigiUniversity of Bologna0000-0001-6014-6421
Depth Stereo non-Lambertian Unbalanced High-resolution
Deposit date
25 Mar 2022 14:24
Last modified
31 May 2022 21:00

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