Evaluation of Intrinsic Image Algorithms to Detect the Shadows Cast by Static Objects Outdoors

Abstract

Abstract:In some automatic scene analysis applications, the presence of shadows becomes a nuisance that is necessary to deal with. As a consequence, a preliminary stage in many computer vision algorithms is to attenuate their effect. In this paper, we focus our attention on the detection of shadows cast by static objects outdoors, as the scene is viewed for extended periods of time (days, weeks) from a fixed camera and considering daylight intervals where the main source of light is the sun. In this context, we report two contributions. First, we introduce the use of synthetic images for which ground truth can be generated automatically, avoiding the tedious effort of manual annotation. Secondly, we report a novel application of the intrinsic image concept to the automatic detection of shadows cast by static objects in outdoors. We make both a quantitative and a qualitative evaluation of several algorithms based on this image representation. For the quantitative evaluation, we used the synthetic data set, while for the qualitative evaluation we used both data sets. Our experimental results show that the evaluated methods can partially solve the problem of shadow detection.

OutCast: Outdoor Single‐image Relighting with Cast Shadows

“…Such methods can identify (and subsequently remove [QTH∗17a]) source shadows and shading in the scenes and can now run at an interactive frame rate [MSZ∗21]. Many early works adopted this approach [ISR12] to enable some form of lighting editing. Unfortunately, it is not obvious how these methods can be extended to produce the inverse, shadow generation, especially with long range interaction.…”

OutCast: Outdoor Single-image Relighting with Cast Shadows

“…Such methods can identify (and subsequently remove [QTH * 17a]) source shadows and shading in the scenes and can now run at an interactive frame rate [MSZ * 21]. Many early works adopted this approach [ISR12] to enable some form of lighting editing. Unfortunately, it is not obvious how these methods can be extended to produce the inverse, shadow generation, especially with long range interaction.…”

ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

“…Both traditional and recent approaches may fail to disentangle different photometric effects like surface albedo transitions and strong cast shadows [4], [7], [17], [5], [19], [10]. In fact, [28] shows that intrinsic image decomposition methods perform relatively weak in detecting cast shadows. To separate these photometric effects (i.e.…”