  Research into dynamic 3D scene understanding has primarily focused on
short-term change tracking from dense observations, while little attention has
been paid to long-term changes with sparse observations. We address this gap
with MoRE, a novel approach for multi-object relocalization and reconstruction
in evolving environments. We view these environments as "living scenes" and
consider the problem of transforming scans taken at different points in time
into a 3D reconstruction of the object instances, whose accuracy and
completeness increase over time. At the core of our method lies an
SE(3)-equivariant representation in a single encoder-decoder network, trained
on synthetic data. This representation enables us to seamlessly tackle instance
matching, registration, and reconstruction. We also introduce a joint
optimization algorithm that facilitates the accumulation of point clouds
originating from the same instance across multiple scans taken at different
points in time. We validate our method on synthetic and real-world data and
demonstrate state-of-the-art performance in both end-to-end performance and
individual subtasks.
