Reliability-Aware Monocular Depth Supervision for Sparse-View Neural Reconstruction
In outdoor driving scenes, sparse-view neural reconstruction is challenging. This paper proposes using Depth Anything V2 as a monocular depth prior, with selective supervision via photometric masks. On Splatfacto, PSNR improves from 14.903 to 15.932 and RMSE drops from 0.542 to 0.100, showing gains from selecting reliable low-error regions.
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[Submitted on 27 Jun 2026]
Title:Reliability-Aware Monocular Depth Supervision for Sparse-View Neural Reconstruction
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Abstract:Sparse-view neural reconstruction is challenging in outdoor driving scenes, where cameras usually move along a narrow forward-facing trajectory and provide limited multi-view overlap. Although monocular depth estimators can provide dense geometric priors, their predictions are noisy, and not uniformly reliable across image regions. In this work, we study monocular depth supervision for sparse-view neural reconstruction. We use Depth Anything V2 as a dense monocular depth prior, align its predictions to metric depth using scale-shift fitting, and apply depth supervision selectively through photometric masks generated from an RGB-only baseline model. We evaluate this strategy on two representative scene representations: Mip-NeRF-360 and Splatfacto. On KITTISeq02 under an every2 sparse-view setting, masked monocular depth supervision gives only marginal rendering gains for Mip-NeRF-360 and does not improve metric geometry. In contrast, Splatfacto benefits more clearly, improving PSNR from 14.903 to 15.932 and reducing RMSE from 0.542 to 0.100. Additional KITTISeq05 experiments and matched-ratio mask ablations further show that the gains for Splatfacto come from selecting reliable low-error regions rather than simply reducing the number of depth-supervised pixels. Additional experiments on the Bicycle scene show that depth supervision can improve geometry while hurting RGB rendering quality when multi-view coverage is already strong. Overall, our results suggest that monocular depth priors are useful for under-constrained sparse-view reconstruction, but should be applied selectively and with moderate weighting.
Comments: 10 pages, 6 figures. All authors contributed equally
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
Cite as: arXiv:2607.02554 [cs.CV]
(or arXiv:2607.02554v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.02554
arXiv-issued DOI via DataCite
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From: Wei-Teng Chu [view email] [v1] Sat, 27 Jun 2026 07:39:22 UTC (3,952 KB)
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