[Submitted on 30 Sep 2026]
Title:ObjectTwin: Measuring the Asset Correspondence Gap for Capture-Anchored Digital Twins
View a PDF of the paper titled ObjectTwin: Measuring the Asset Correspondence Gap for Capture-Anchored Digital Twins, by Zhenghe Guo
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Abstract:Object-wise reconstruction increasingly inserts generated meshes into captured scenes as editable proxies for observed objects. This use creates a requirement absent from isolated-asset scores and pose estimates: at the captured metric state, the proxy should reproduce calibrated observations of the same instance. We present ObjectTwin, a scan-grounded measurement protocol that freezes the instance, cameras, scan-projected support and recorded depth, metric anchor, and evaluation denominator, then replaces only the object representation. The resulting asset correspondence gap measures lost observation agreement relative to scan-derived visible geometry without treating the scan as complete physical ground truth. Across two indoor RGB-D capture domains and two generation backends, visible, anchored generated assets lose 0.302-0.497 MaskIoU under this replacement. Every tested scene-level estimate is positive, while same-input generator preference reverses across capture domains. Coverage and overlap-depth analyses locate disagreement in both visible support and surface placement; the evaluated conservative pose refinement does not systematically recover it. These results establish capture correspondence as a separately measurable interface between generated-asset quality and stronger physical or task-level evaluation.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.35904 [cs.CV]
(or arXiv:2610.35904v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.35904
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zhenghe Guo [view email] [v1] Wed, 30 Sep 2026 02:01:37 UTC (7,211 KB)
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