[Submitted on 27 Jul 2026]
Title:Spatial Lifting for Dense Prediction
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Abstract:We present Spatial Lifting (SL), a novel methodology for dense prediction tasks. SL operates by lifting standard inputs, such as 2D images, into a higher-dimensional space and subsequently processing them using networks designed for that higher dimension, such as a 3D U-Net. Counterintuitively, this dimensionality lifting allows us to achieve good performance on benchmark tasks compared to conventional approaches, while reducing inference costs and \textbf{drastically lowering the number of model parameters}. The SL framework produces intrinsically structured outputs along the lifted dimension. This emergent structure facilitates dense supervision during training and enables single-forward-pass self-consistency-based quality and uncertainty estimation at test time. Spatial Lifting introduces a simple and general modeling strategy that offers a promising path toward more efficient, accurate, and reliable deep networks for dense prediction tasks in vision.
Comments: 28 pages 5 figures
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2610.00017 [cs.CV]
(or arXiv:2610.00017v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.00017
arXiv-issued DOI via DataCite
Submission history
From: Mingzhi Xu [view email] [v1] Mon, 27 Jul 2026 15:22:14 UTC (10,029 KB)
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