Booster Dataset - Monocular Split

Zama Ramirez, Pierluigi ; Tosi, Fabio ; Poggi, Matteo ; Alex, Costanzino ; Salti, Samuele ; Mattoccia, Stefano ; Di Stefano, Luigi (2023) Booster Dataset - Monocular Split. University of Bologna. DOI 10.6092/unibo/amsacta/7161. [Dataset]
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Estimating depth from images nowadays yields outstanding results, both in terms of in-domain accuracy and generalization. However, we identify two main challenges that remain open in this field: dealing with non-Lambertian materials and effectively processing high-resolution images. Purposely, we propose a novel dataset that includes accurate and dense ground-truth labels at high resolution, featuring scenes containing several specular and transparent surfaces. Our acquisition pipeline leverages a novel deep space-time stereo framework, enabling easy and accurate labeling with sub-pixel precision. The dataset is composed of 606 samples collected in 85 different scenes, each sample includes both a high-resolution pair (12 Mpx) as well as an unbalanced stereo pair (Left: 12 Mpx, Right: 1.1 Mpx). Additionally, we provide manually annotated material segmentation masks and 15K unlabeled samples. We divide the dataset into a training set, and two testing sets, the latter devoted to the evaluation of stereo and monocular depth estimation networks respectively to highlight the open challenges and future research directions in this field.

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Zama Ramirez, PierluigiUniversity of Bologna0000-0001-7734-5064
Tosi, FabioUniversity of Bologna0000-0002-6276-5282
Poggi, MatteoUniversity of Bologna0000-0002-3337-2236
Alex, CostanzinoUniversity of Bologna0000-0001-9859-8482
Salti, SamueleUniversity of Bologna0000-0001-5609-426X
Mattoccia, StefanoUniversity of Bologna0000-0002-3681-7704
Di Stefano, LuigiUniversity of Bologna0000-0001-6014-6421
Depth Monocular non-Lambertian Unbalanced High-resolution
Deposit date
10 Feb 2023 08:50
Last modified
02 Apr 2023 21:00
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