Has Anything Changed? 3D Change Detection by 2D Segmentation Masks
Aikaterini Adam Konstantinos Karantzalos Lazaros Grammatikopoulos Torsten Sattler
As capturing devices become common, 3D scans of interior spaces are acquired
on a daily basis. Through scene comparison over time, information about objects
in the scene and their changes is inferred. This information is important for
robots and AR and VR devices, in order to operate in an immersive virtual
experience. We thus propose an unsupervised object discovery method that
identifies added, moved, or removed objects without any prior knowledge of what
objects exist in the scene. We model this problem as a combination of a 3D
change detection and a 2D segmentation task. Our algorithm leverages generic 2D
segmentation masks to refine an initial but incomplete set of 3D change
detections. The initial changes, acquired through render-and-compare likely
correspond to movable objects. The incomplete detections are refined through
graph optimization, distilling the information of the 2D segmentation masks in
the 3D space. Experiments on the 3Rscan dataset prove that our method
outperforms competitive baselines, with SoTA results.