Monte Carlo Scene Search for 3D Scene Understanding
This addresses the problem of data-efficient 3D scene understanding for computer vision researchers, though it is incremental as it modifies an existing algorithm for a new application.
The paper tackles 3D scene understanding from noisy RGB-D scans by adapting Monte Carlo Tree Search (MCTS) to retrieve objects and room layouts, reducing the need for training data. It demonstrates on ScanNet that the method often retrieves configurations better than manual annotations, especially for layouts.
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.