# Rainy screens: Collecting rainy datasets, indoors

Source: [Rainy screens: Collecting rainy datasets, indoors](<https://www.sota2.com/research/paper/68fa67371e209131cea14eb6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:09:20.078Z

Authors: Horia Porav, Valentina-Nicoleta Musat, Tom Bruls, Paul Newman

Published: 2020-03-10T13:57:37.000Z

## Abstract

Acquisition of data with adverse conditions in robotics is a cumbersome task due to the difficulty in guaranteeing proper ground truth and synchronising with desired weather conditions. In this paper, we present a simple method - recording a high resolution screen - for generating diverse rainy images from existing clear ground-truth images that is domain- and source-agnostic, simple and scales up. This setup allows us to leverage the diversity of existing datasets with auxiliary task ground-truth data, such as semantic segmentation, object positions etc. We generate rainy images with real adherent droplets and rain streaks based on Cityscapes and BDD, and train a de-raining model. We present quantitative results for image reconstruction and semantic segmentation, and qualitative results for an out-of-sample domain, showing that models trained with our data generalize well.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2003.04742>)

[PDF](<https://arxiv.org/pdf/2003.04742.pdf>)