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Spatial Cognition from Egocentric Video: Out of Sight, Not Out of Mind

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arxiv 2404.05072 v2 pith:YL6KFSPW submitted 2024-04-07 cs.CV

classification cs.CV
keywords objectsegocentriccognitionlongsightspatialtheyactive
verification ladder T0 review T1 audit T2 compute T3 formal
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As humans move around, performing their daily tasks, they are able to recall where they have positioned objects in their environment, even if these objects are currently out of their sight. In this paper, we aim to mimic this spatial cognition ability. We thus formulate the task of Out of Sight, Not Out of Mind - 3D tracking active objects using observations captured through an egocentric camera. We introduce a simple but effective approach to address this challenging problem, called Lift, Match, and Keep (LMK). LMK lifts partial 2D observations to 3D world coordinates, matches them over time using visual appearance, 3D location and interactions to form object tracks, and keeps these object tracks even when they go out-of-view of the camera. We benchmark LMK on 100 long videos from EPIC-KITCHENS. Our results demonstrate that spatial cognition is critical for correctly locating objects over short and long time scales. E.g., for one long egocentric video, we estimate the 3D location of 50 active objects. After 120 seconds, 57% of the objects are correctly localised by LMK, compared to just 33% by a recent 3D method for egocentric videos and 17% by a general 2D tracking method.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Generating 6DoF Object Manipulation Trajectories from Action Description in Egocentric Vision

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A new 28,497-sample dataset of 6DoF object manipulation trajectories is automatically extracted from egocentric video, and vision-language models are trained to generate these trajectories from action descriptions.

  2. Reconstructing 4D Spatial Intelligence: A Survey

    cs.CV 2025-07 accept novelty 4.0 of 10

    A review that classifies 4D scene reconstruction methods into five progressive levels: low-level cues, scene components, dynamic scenes, interactions, and physics.

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