  The advent of wearable computers enables a new source of context for AI that
is embedded in egocentric sensor data. This new egocentric data comes equipped
with fine-grained 3D location information and thus presents the opportunity for
a novel class of spatial foundation models that are rooted in 3D space. To
measure progress on what we term Egocentric Foundation Models (EFMs) we
establish EFM3D, a benchmark with two core 3D egocentric perception tasks.
EFM3D is the first benchmark for 3D object detection and surface regression on
high quality annotated egocentric data of Project Aria. We propose Egocentric
Voxel Lifting (EVL), a baseline for 3D EFMs. EVL leverages all available
egocentric modalities and inherits foundational capabilities from 2D foundation
models. This model, trained on a large simulated dataset, outperforms existing
methods on the EFM3D benchmark.
