  Measurement update rules for Bayes filters often contain hand-crafted
heuristics to compute observation probabilities for high-dimensional sensor
data, like images. In this work, we propose the novel approach Deep Measurement
Update (DMU) as a general update rule for a wide range of systems. DMU has a
conditional encoder-decoder neural network structure to process depth images as
raw inputs. Even though the network is trained only on synthetic data, the
model shows good performance at evaluation time on real-world data. With our
proposed training scheme primed data training , we demonstrate how the DMU
models can be trained efficiently to be sensitive to condition variables
without having to rely on a stochastic information bottleneck. We validate the
proposed methods in multiple scenarios of increasing complexity, beginning with
the pose estimation of a single object to the joint estimation of the pose and
the internal state of an articulated system. Moreover, we provide a benchmark
against Articulated Signed Distance Functions(A-SDF) on the RBO dataset as a
baseline comparison for articulation state estimation.
