Booster: a Benchmark for Depth from Images of Specular and Transparent Surfaces
This addresses a specific challenge in computer vision for researchers working on depth estimation with non-Lambertian materials, but it is incremental as it primarily provides a new dataset rather than a novel method.
The authors tackled the problem of depth estimation for specular and transparent surfaces by creating a high-resolution dataset with accurate ground-truth labels, resulting in a collection of 606 samples across 85 scenes, including 12 Mpx images and material segmentation masks.
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), typical of modern mobile devices that mount sensors with different resolutions. Additionally, we provide manually annotated material segmentation masks and 15K unlabeled samples. The dataset is composed of a train set and two test sets, the latter devoted to the evaluation of stereo and monocular depth estimation networks. Our experiments highlight the open challenges and future research directions in this field.