On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities
This addresses the challenge of recognizing transparent objects in domestic scenarios, which is an incremental improvement for household robotics and automation.
The paper tackled the problem of recognizing transparent objects in domestic environments, which current methods fail on, by using a fusion of multiple sensor modalities to capture specular reflectance and unavailable depth information, resulting in a significant increase in robustness of recognition over a larger set of commonly used objects.
Current object recognition methods fail on object sets that include both diffuse, reflective and transparent materials, although they are very common in domestic scenarios. We show that a combination of cues from multiple sensor modalities, including specular reflectance and unavailable depth information, allows us to capture a larger subset of household objects by extending a state of the art object recognition method. This leads to a significant increase in robustness of recognition over a larger set of commonly used objects.