  Object pose recovery has gained increasing attention in the computer vision
field as it has become an important problem in rapidly evolving technological
areas related to autonomous driving, robotics, and augmented reality. Existing
review-related studies have addressed the problem at visual level in 2D, going
through the methods which produce 2D bounding boxes of objects of interest in
RGB images. The 2D search space is enlarged either using the geometry
information available in the 3D space along with RGB (Mono/Stereo) images, or
utilizing depth data from LIDAR sensors and/or RGB-D cameras. 3D bounding box
detectors, producing category-level amodal 3D bounding boxes, are evaluated on
gravity aligned images, while full 6D object pose estimators are mostly tested
at instance-level on the images where the alignment constraint is removed.
Recently, 6D object pose estimation is tackled at the level of categories. In
this paper, we present the first comprehensive and most recent review of the
methods on object pose recovery, from 3D bounding box detectors to full 6D pose
estimators. The methods mathematically model the problem as a classification,
regression, classification & regression, template matching, and point-pair
feature matching task. Based on this, a mathematical-model-based categorization
of the methods is established. Datasets used for evaluating the methods are
investigated with respect to the challenges, and evaluation metrics are
studied. Quantitative results of experiments in the literature are analyzed to
show which category of methods best performs across what types of challenges.
The analyses are further extended comparing two methods, which are our own
implementations, so that the outcomes from the public results are further
solidified. Current position of the field is summarized regarding object pose
recovery, and possible research directions are identified.
