Reliability-Aware Monocular Depth Supervision for Sparse-View Neural Reconstruction
Wei-Teng Chu, Yashasvini Gopalan, Changju Yuan
Abstract
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.
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Classified with taxonomy v2 on Sat, 5 Sept 2026.