Asia
WT-MVSNet: Window-basedTransformersfor Multi-viewStereo
Arecenteffort toperform attention-based matching alongtheepipolar linesofsourceimages [32],suffersinstead from sensitivity to inaccurate camera pose and calibration, which can in turn results to erroneous matching. Another key step in contemporary learned MVS methods is the regularization of cost volume, generated by stacking cost maps associated with respective depth hypotheses.
AProvablyEfficientSampleCollectionStrategy forReinforcementLearning
One of the challenges inonline reinforcement learning (RL) is that the agent needs to trade off the exploration of the environment and the exploitation of the samples to optimize its behavior. Whether we optimize for regret, sample complexity, state-space coverage or model estimation, we need to strike a different exploration-exploitation trade-off.