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Research into dynamic 3D scene understanding has primarily focused onshort-term change tracking from dense observations, while little attention hasbeen paid to long-term changes with sparse observations. We address this gapwith MoRE, a novel approach for multi-object relocalization and reconstructionin evolving environments. We view these environments as "living scenes" andconsider the problem of transforming scans taken at different points in timeinto a 3D reconstruction of the object instances, whose accuracy andcompleteness increase over time. At the core of our method lies anSE(3)-equivariant representation in a single encoder-decoder network, trainedon synthetic data. This representation enables us to seamlessly tackle instancematching, registration, and reconstruction. We also introduce a jointoptimization algorithm that facilitates the accumulation of point cloudsoriginating from the same instance across multiple scans taken at differentpoints in time. We validate our method on synthetic and real-world data anddemonstrate state-of-the-art performance in both end-to-end performance andindividual subtasks.

Living Scenes: Multi-object Relocalization and Reconstruction in Changing 3D Environments