  We study the task of 3D multi-object re-identification from embodied tours.
Specifically, an agent is given two tours of an environment (e.g. an apartment)
under two different layouts (e.g. arrangements of furniture). Its task is to
detect and re-identify objects in 3D - e.g. a "sofa" moved from location A to
B, a new "chair" in the second layout at location C, or a "lamp" from location
D in the first layout missing in the second. To support this task, we create an
automated infrastructure to generate paired egocentric tours of
initial/modified layouts in the Habitat simulator using Matterport3D scenes,
YCB and Google-scanned objects. We present 3D Semantic MapNet (3D-SMNet) - a
two-stage re-identification model consisting of (1) a 3D object detector that
operates on RGB-D videos with known pose, and (2) a differentiable object
matching module that solves correspondence estimation between two sets of 3D
bounding boxes. Overall, 3D-SMNet builds object-based maps of each layout and
then uses a differentiable matcher to re-identify objects across the tours.
After training 3D-SMNet on our generated episodes, we demonstrate zero-shot
transfer to real-world rearrangement scenarios by instantiating our task in
Replica, Active Vision, and RIO environments depicting rearrangements. On all
datasets, we find 3D-SMNet outperforms competitive baselines. Further, we show
jointly training on real and generated episodes can lead to significant
improvements over training on real data alone.
