1 citations · 2 across the 4 of their papers we have counts for
3 papers · 1 filter
Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF
Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt +1
We solve the problem of 6-DoF localisation and 3D dense reconstruction in spatial environments as approximate Bayesian inference in a deep state-space model. Our approach leverages…
Approximate Bayesian inference in spatial environments
Atanas Mirchev, Baris Kayalibay, Maximilian Soelch +2
Model-based approaches bear great promise for decision making of agents interacting with the physical world. In the context of spatial environments, different types of problems suc…
Classification of sparsely labeled spatio-temporal data through semi-supervised adversarial learning
Atanas Mirchev, Seyed-Ahmad Ahmadi
In recent years, Generative Adversarial Networks (GAN) have emerged as a powerful method for learning the mapping from noisy latent spaces to realistic data samples in high-dimensi…