2 citations · 3 across the 5 of their papers we have counts for
5 papers
Net: Semantic-Aware Self-supervised Depth Estimation with Monocular Videos and Synthetic Data
Bin Cheng, Inderjot Singh Saggu, Raunak Shah +2
Solving depth estimation with monocular cameras enables the possibility of widespread use of cameras as low-cost depth estimation sensors in applications such as autonomous driving…
SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception
Yue Meng, Yongxi Lu, Aman Raj +5
Unsupervised learning for geometric perception (depth, optical flow, etc.) is of great interest to autonomous systems. Recent works on unsupervised learning have made considerable…
Online Learning for Position-Aided Millimeter Wave Beam Training
Vutha Va, Takayuki Shimizu, Gaurav Bansal +1
Accurate beam alignment is essential for beam-based millimeter wave communications. Conventional beam sweeping solutions often have large overhead, which is unacceptable for mobile…
Inverse Multipath Fingerprinting for Millimeter Wave V2I Beam Alignment
Vutha Va, Junil Choi, Takayuki Shimizu +2
Efficient beam alignment is a crucial component in millimeter wave systems with analog beamforming, especially in fast-changing vehicular settings. This paper proposes a position-a…
Adaptive Content Control for Communication amongst Cooperative Automated Vehicles
Mohammad Fanaei, Amin Tahmasbi-Sarvestani, Yaser P. Fallah +3
Cooperative automated vehicles exchange information to assist each other in creating a more precise and extended view of their surroundings, with the aim of improving automated-dri…