2026-07-17 04:00 UTCOriginal source2 min readUpdated: 2026-07-17 08:28 UTC

SeeSE3: Emergence of 3D Space in Vision Features

This paper investigates whether vision foundation models build representations that reflect intrinsic properties of 3D Euclidean space. Instead of regressing depth or normals, the authors probe the relationship between visual feature space structure and Euclidean transformation group SE(3) using a mutual neighborhood metric and a Poincaré Adapter. They show that self-supervised vision models harbor latent subspaces strongly correlated with 3D space, even without 3D supervision. This leads to 'Latent-Space Navigation' techniques for visual odometry and localization without explicit 3D reconstruction.

SourcearXiv Computer VisionAuthor: Caroline Chen, Sayna Ebrahimi, Fedor Kitashov, Ming-Hsuan Yang, Leonidas Guibas, Viorica P\u{a}tr\u{a}ucean, Maks Ovsjanikov

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[Submitted on 15 Jul 2026]

Title:SeeSE3: Emergence of 3D Space in Vision Features

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Abstract:In this paper, we ask whether vision foundation models construct representations that reflect the intrinsic properties of 3D Euclidean space. Unlike previous works that probe 3D awareness of vision features by regressing image-centric quantities such as depth or normals, we investigate the relation between the structure of the space of visual features and the group of Euclidean transformations $SE(3)$. We propose a set of probes to evaluate this relation from both topological and geometric perspectives: a mutual neighborhood metric that measures the alignment between feature neighborhoods and spatial topology, and a Poincaré Adapter to test the linear accessibility of the geometry of camera motion from latent displacements in static scenes. We show that self-supervised vision models, which, in principle, have not been trained with direct 3D supervision or active agency, possess latent subspaces that are remarkably strongly correlated with three-dimensional Euclidean space, when probed correctly. Building on this insight we propose a new class of "Latent-Space Navigation" techniques that perform visual odometry and localization purely in the latent space, bypassing the need for explicit 3D reconstruction.

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

Cite as: arXiv:2607.14228 [cs.CV]

(or arXiv:2607.14228v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.14228

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Maks Ovsjanikov [view email] [v1] Wed, 15 Jul 2026 18:00:31 UTC (8,563 KB)

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