Title: Depth in Motion: Robust Self-Supervised Learning via Representation-Optimization-Supervision Synergy
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Self-supervised monocular depth estimation, Dynamic scene understanding, Cost-volume consistency, Robust photometric supervision, Frequency-domain uncertainty modeling

Abstract: Self-supervised monocular depth estimation recovers scene geometry from unlabeled monocular videos, yet its reliance on photometric constancy tends to cause failures in dynamic scenes: motion and occlusion corrupt correspondences, bias optimization toward texture-sparse regions, and drive residuals into heavy-tailed distributions that undermine supervision. To address these challenges, we propose a Representation–Optimization–Supervision Synergy Network (ROSS-Net), which establishes a holistic defense by restructuring the entire estimation flow to mitigate interlinked failure modes. At the representation level, the Spatio-Temporal Epipolar Calibrator (STEC) validates correspondences across appearance, feature, and temporal cues to filter motion-induced mismatches while preserving dynamic evidence. At the optimization level, the Entropy-Guided Spectral Integrator (EGSI) calibrates depth-axis spectra to counter low-frequency optimization bias while adding no inference-time overhead. At the supervision level, the Order-Statistic Consensus Operator (OSCO) trims and reweights outlier residuals, converting noisy reprojections into robust supervision. Experiments on KITTI and NYUv2 show that ROSS-Net significantly outperforms prior methods under motion and occlusion, and generalizes strongly to unseen domains such as Make3D and ScanNet.