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EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation Models

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arxiv 2406.10224 v1 pith:DSZ7ICA5 submitted 2024-06-14 cs.CV

Julian Straub , Daniel DeTone , Tianwei Shen , Nan Yang , Chris Sweeney , Richard Newcombe This is my paper
classification cs.CV
keywords egocentricbenchmarkefm3dfoundationmodelsdataefmsprogress
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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