EgoLifter: Open-world 3D Segmentation for Egocentric Perception
Qiao Gu Zhaoyang Lv Duncan Frost Simon Green Julian Straub Chris Sweeney
In this paper we present EgoLifter, a novel system that can automatically
segment scenes captured from egocentric sensors into a complete decomposition
of individual 3D objects. The system is specifically designed for egocentric
data where scenes contain hundreds of objects captured from natural
(non-scanning) motion. EgoLifter adopts 3D Gaussians as the underlying
representation of 3D scenes and objects and uses segmentation masks from the
Segment Anything Model (SAM) as weak supervision to learn flexible and
promptable definitions of object instances free of any specific object
taxonomy. To handle the challenge of dynamic objects in ego-centric videos, we
design a transient prediction module that learns to filter out dynamic objects
in the 3D reconstruction. The result is a fully automatic pipeline that is able
to reconstruct 3D object instances as collections of 3D Gaussians that
collectively compose the entire scene. We created a new benchmark on the Aria
Digital Twin dataset that quantitatively demonstrates its state-of-the-art
performance in open-world 3D segmentation from natural egocentric input. We run
EgoLifter on various egocentric activity datasets which shows the promise of
the method for 3D egocentric perception at scale.