By extension, effective representation of material and texture can describe other semantic classes strongly associated with said material and texture.
That is, we show that the unique polarization pattern encoded in the polarimetric appearance of an object captured under the sky can be decoded to reveal the surface normal at each pixel.
To address the mounting destruction caused by floods in climate-vulnerable regions, we propose Street to Cloud, a machine learning pipeline for incorporating crowdsourced ground truth data into the segmentation of satellite imagery of floods.
We demonstrate that H2O-Net outperforms the state-of-the-art semantic segmentation methods on satellite imagery by 10% and 12% pixel accuracy and mIoU respectively for the task of flood segmentation.
We demonstrate the increased performance of AngLNet over prior state-of-the-art in material segmentation from satellite imagery.
The connection between visual input and tactile sensing is critical for object manipulation tasks such as grasping and pushing.
The residuals are computed by differencing the sparse-sampled reflectance function with a dictionary of pre-defined dense-sampled reflectance functions.