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

arXiv:2607.02554v1 Announce Type: new 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 ...