HOC-Search: Efficient CAD Model and Pose Retrieval from RGB-D Scans
Stefan Ainetter Sinisa Stekovic Friedrich Fraundorfer Vincent Lepetit
We present an automated and efficient approach for retrieving high-quality
CAD models of objects and their poses in a scene captured by a moving RGB-D
camera. We first investigate various objective functions to measure similarity
between a candidate CAD object model and the available data, and the best
objective function appears to be a "render-and-compare" method comparing depth
and mask rendering. We thus introduce a fast-search method that approximates an
exhaustive search based on this objective function for simultaneously
retrieving the object category, a CAD model, and the pose of an object given an
approximate 3D bounding box. This method involves a search tree that organizes
the CAD models and object properties including object category and pose for
fast retrieval and an algorithm inspired by Monte Carlo Tree Search, that
efficiently searches this tree. We show that this method retrieves CAD models
that fit the real objects very well, with a speed-up factor of 10x to 120x
compared to exhaustive search.