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OmniHorizon: In-the-Wild Outdoors Depth and Normal Estimation from Synthetic Omnidirectional Dataset
Finally, we demonstrate in-the-wild depth and normal estimation on real-world images with UBotNet trained purely on our OmniHorizon dataset, showing the promise of proposed dataset and network for scene understanding.
VisTaNet: Attention Guided Deep Fusion for Surface Roughness Classification
Our study shows that analogous to human texture perception, the proposed model chooses a weighted combination of the two modalities (visual and tactile), thus resulting in higher surface roughness classification accuracy; and it chooses to maximize the weightage of the tactile modality where the visual modality fails and vice-versa.
Towards Multidimensional Textural Perception and Classification Through Whisker
Further, we experimentally validate that the sensor can classify texture with roughness depths as low as $2. 5\mu m$ at an accuracy of $90\%$ or more and segregate materials based on their roughness and hardness.
A Robust and Scalable Attention Guided Deep Learning Framework for Movement Quality Assessment
Four novel feature extractors are proposed and studied that allow the transformer network to operate on skeletal data.
