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Deployment of deep learning models in robotics as sensory information extractors can be a daunting task to handle, even using generic GPU cards.
Ranked #5 on Semantic Segmentation on NYU Depth v2
To address these challenges, we present ClearGrasp -- a deep learning approach for estimating accurate 3D geometry of transparent objects from a single RGB-D image for robotic manipulation.
Visual perception entails solving a wide set of tasks (e. g., object detection, depth estimation, etc).
Ranked #1 on Depth Estimation on Taskonomy
We present a dataset of $360^o$ images of indoor spaces with their corresponding ground truth surface normal, and train a deep convolutional neural network (CNN) on the task of monocular 360 surface estimation.