  Object detection and 6D pose estimation in the crowd (scenes with multiple
object instances, severe foreground occlusions and background distractors), has
become an important problem in many rapidly evolving technological areas such
as robotics and augmented reality. Single shot-based 6D pose estimators with
manually designed features are still unable to tackle the above challenges,
motivating the research towards unsupervised feature learning and
next-best-view estimation. In this work, we present a complete framework for
both single shot-based 6D object pose estimation and next-best-view prediction
based on Hough Forests, the state of the art object pose estimator that
performs classification and regression jointly. Rather than using manually
designed features we a) propose an unsupervised feature learnt from
depth-invariant patches using a Sparse Autoencoder and b) offer an extensive
evaluation of various state of the art features. Furthermore, taking advantage
of the clustering performed in the leaf nodes of Hough Forests, we learn to
estimate the reduction of uncertainty in other views, formulating the problem
of selecting the next-best-view. To further improve pose estimation, we propose
an improved joint registration and hypotheses verification module as a final
refinement step to reject false detections. We provide two additional
challenging datasets inspired from realistic scenarios to extensively evaluate
the state of the art and our framework. One is related to domestic environments
and the other depicts a bin-picking scenario mostly found in industrial
settings. We show that our framework significantly outperforms state of the art
both on public and on our datasets.
