# Reliability-Aware Monocular Depth Supervision for Sparse-View Neural Reconstruction

arXiv: https://arxiv.org/abs/2607.02554 · HTML: https://mlsanity.com/paper/2607.02554/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 24 references that are in the map.
- First posted on arXiv 27 Jun 2026.

## Builds on (most cited first, 20 of 20)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [Depth-supervised NeRF: Fewer Views and Faster Training for Free](https://mlsanity.com/paper/2107.02791/) (2021)
- [pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction](https://mlsanity.com/paper/2312.12337/) (2023)
- [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://mlsanity.com/paper/2302.12288/) (2023)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images](https://mlsanity.com/paper/2403.14627/) (2024)
- [UniDepth: Universal Monocular Metric Depth Estimation](https://mlsanity.com/paper/2403.18913/) (2024)
- [Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image](https://mlsanity.com/paper/2307.10984/) (2023)
- [Dense Depth Priors for Neural Radiance Fields from Sparse Input Views](https://mlsanity.com/paper/2112.03288/) (2021)
- [DepthSplat: Connecting Gaussian Splatting and Depth](https://mlsanity.com/paper/2410.13862/) (2024)
- [DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization](https://mlsanity.com/paper/2403.06912/) (2024)
- [Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images](https://mlsanity.com/paper/2311.13398/) (2023)
- [DN-Splatter: Depth and Normal Priors for Gaussian Splatting and Meshing](https://mlsanity.com/paper/2403.17822/) (2024)
- [Digging into Depth Priors for Outdoor Neural Radiance Fields](https://mlsanity.com/paper/2308.04413/) (2023)
- [In Depth We Trust: Reliable Monocular Depth Supervision for Gaussian Splatting](https://mlsanity.com/paper/2604.05715/) (2026)

## Abstract (arXiv)

Sparse-view neural reconstruction in outdoor driving is challenging due to narrow forward-facing trajectories and limited multi-view overlap, and monocular depth priors, though dense, are noisy and not uniformly reliable. We use Depth Anything V2 (DA-V2) as a dense monocular depth prior, align its per-image scale and shift to metric depth using sparse anchors (LiDAR and COLMAP) and apply depth supervision selectively through photometric masks generated from an RGB-only baseline model, and evaluate on Mip-NeRF-360 and Splatfacto. On KITTISeq02, masked depth supervision gives only marginal gains for Mip-NeRF-360 and does not improve geometry. In contrast, Splatfacto benefits clearly, improving PSNR from 14.903 to 15.932 and reducing RMSE from 0.542 to 0.100. Against global supervision, the proposed mask achieves 0.44-0.70,dB PSNR gains across KITTI sequences 00/02/05 at tied or better RMSE, while yielding no change on Mip-NeRF-360. This indicates the mask primarily enhances rendering fidelity rather than geometry. Matched-ratio ablations and two further KITTI fragments confirm the gains come from selecting reliable low-error regions, rather than from fewer pixels. On the Bicycle scene, depth supervision improves geometry but hurts RGB rendering quality when multi-view coverage is already strong. Using DA-V2 as a representative prior, results suggest that monocular depth priors are valuable for under-constrained sparse-view reconstruction when applied selectively with moderate weighting.