Opti3R: Geometry-Locked Optical Pairing and Reliability-Weighted Cross-View Supervision for Underwater 3D Reconstruction

Attenuation and backscatter change underwater image appearance even when the scene geometry stays fixed. These changes can lead feed-forward models to predict inconsistent depth and shape. To reduce this effect, we propose Opti3R, which trains on Geometry-Locked Optical Pairing (GLOP): paired reference and optically perturbed images with consistent geometry targets. We further use teacher correspondences to supervise cross-view consistency with continuous visibility-confidence-reprojection (VCR) edge weighting, referred to as VCR Graph supervision (VCRG). Our proposed Axial-Channel Geometry Adapter (ACGA) adds identity-initialized multiscale residual blocks to the dense depth and point heads. These blocks take dense features as input and are optimized jointly with the GLOP and VCRG objectives. Raw inference achieves absolute relative depth errors of 0.0838 on SQUID and 0.0496 on FLSea-VI. GLOP gives lower reconstruction error than generic and unpaired augmentation. Opti3R also shows slower growth in geometry drift under held-out optical conditions, and higher VCR edge weights are associated with lower independent geometric disagreement. These results support geometry-locked optical pairing and continuously weighted cross-view supervision for underwater reconstruction.