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.

我们提出了一个新颖的高分辨率、充满挑战性的立体数据集，并给出了密集准确的视差地面真值标注。该数据集独特之处在于存在一些具有镜面反射和透明性质的表面，这是当前立体网络失败的主要原因。我们的获取方法采用了一个新颖的深度时空立体框架，可以实现子像素精度的简单准确标注。我们释放了总共 419 个样品，收集自 64 个不同的场景并标注了密集的地面真值视差。每个样本包括一个高分辨率成对图像 (12 Mpx)，以及一个不对称的成对图像 (左边：12 Mpx，右边：1.1 Mpx)。此外，我们提供了手动注释的材质分割掩模和 15K 未标记样本。我们根据我们的数据集评估了最先进的深度网络，强调了它们在解决立体视觉中的开放挑战方面的局限性，并为未来的研究提供了一些线索。

深度立体视觉的挑战：Booster 数据集

Open Challenges in Deep Stereo: the Booster Dataset

We present a method for improving segmentation tasks on images affected by
adherent rain drops and streaks. We introduce a novel stereo dataset recorded
using a system that allows one lens to be affected by real water droplets while
keeping the other lens clear. We train a denoising generator using this dataset
and show that it is effective at removing the effect of real water droplets, in
the context of image reconstruction and road marking segmentation. To further
test our de-noising approach, we describe a method of adding computer-generated
adherent water droplets and streaks to any images, and use this technique as a
proxy to demonstrate the effectiveness of our model in the context of general
semantic segmentation. We benchmark our results using the CamVid road marking
segmentation dataset, Cityscapes semantic segmentation datasets and our own
real-rain dataset, and show significant improvement on all tasks.

本研究提出了一种改进在有附着雨滴和条纹影响的图像分割任务的方法，通过引入新颖的立体数据集并以此进行噪声降低生成器的训练，最终有效去除真实的水滴所产生的影响，并在道路标记分割和语义分割等任务中展现出显著的提高。