  We explore how a general AI algorithm can be used for 3D scene understanding
to reduce the need for training data. More exactly, we propose a modification
of the Monte Carlo Tree Search (MCTS) algorithm to retrieve objects and room
layouts from noisy RGB-D scans. While MCTS was developed as a game-playing
algorithm, we show it can also be used for complex perception problems. Our
adapted MCTS algorithm has few easy-to-tune hyperparameters and can optimise
general losses. We use it to optimise the posterior probability of objects and
room layout hypotheses given the RGB-D data. This results in an
analysis-by-synthesis approach that explores the solution space by rendering
the current solution and comparing it to the RGB-D observations. To perform
this exploration even more efficiently, we propose simple changes to the
standard MCTS' tree construction and exploration policy. We demonstrate our
approach on the ScanNet dataset. Our method often retrieves configurations that
are better than some manual annotations, especially on layouts.
