  The prospect of assistive robots aiding in object organization has always
been compelling. In an image-goal setting, the robot rearranges the current
scene to match the single image captured from the goal scene. The key to an
image-goal rearrangement system is estimating the desired placement pose of
each object based on the single goal image and observations from the current
scene. In order to establish sufficient associations for accurate estimation,
the system should observe an object from a viewpoint similar to that in the
goal image. Existing image-goal rearrangement systems, due to their reliance on
a fixed viewpoint for perception, often require redundant manipulations to
randomly adjust an object's pose for a better perspective. Addressing this
inefficiency, we introduce a novel object rearrangement system that employs
multi-view fusion. By observing the current scene from multiple viewpoints
before manipulating objects, our approach can estimate a more accurate pose
without redundant manipulation times. A standard visual localization pipeline
at the object level is developed to capitalize on the advantages of multi-view
observations. Simulation results demonstrate that the efficiency of our system
outperforms existing single-view systems. The effectiveness of our system is
further validated in a physical experiment.
