Realistic Modeling of Water Droplets for Monocular Adherent Raindrop Recognition Using Bézier Curves

Abstract

Abstract. In this paper, we propose a novel raindrop shape model for the detection of view-disturbing, adherent raindrops on inclined surfaces. Whereas stateof-the-art techniques do not consider inclined surfaces because they assume the droplets as sphere sections with equal contact angles, our model incorporates cubic Bézier curves that provide a low dimensional and physically interpretable representation of a raindrop surface. The parameters are empirically deduced from numerous observations of different raindrop sizes and surface inclination angles. It can be easily integrated into a probabilistic framework for raindrop recognition, using geometrical optics to simulate the visual raindrop appearance. In comparison to a sphere section model, the proposed model yields an improved droplet surface accuracy up to three orders of magnitude.

I Can See Clearly Now: Image Restoration via De-Raining

“…Rain Modelling and Simulation: In the context of computer vision, several studies have attempted to model the structure and optical properties of adherent water droplets. The authors of RIGSEC [17], [18] model raindrops first as sections of a sphere and later account for the effect of gravity using 2D Bezier curves, and confirm experimentally that a physically correct droplet shape can be computed using this method. [19] additionally study and model the dark band around the edges of adherent drops, and show that a simplified model is enough to correctly undistort the image on the surface of the droplet.…”

“…We base our simple synthetic droplet model on the works of [17], [18] and [19], generate the locations of raindrops using a simple statistical approach, model the interactions between raindrops using metaballs [20] and implement its rendering efficiently using GPU shaders.…”

“…Raindrop Detection and Removal: In [17] and [18], raindrops are detected by attempting to match a template of a synthetic raindrop at locations where the presence of a real drop is hypothesized. This approach breaks down when the shape of the real droplets differs significantly from that of the template.…”

Real-time raindrop detection based on cellular neural networks for ADAS

“…Roser and Geiger [26] Attempted to model the shape of adherent raindrops by a sphere crown and later, by Bezier curves [27]. However, these models are insufficient, since a sphere crown and Bezier curves can cover only a small portion of possible raindrop shapes.…”

Removing Rain from a Single Image via Discriminative Sparse Coding

“…[3,4,5,6,9,10,19,20,21,28]. The redundant temporal information existing in videos or multiple consecutive frames provides rich information to identify rain drops.…”