3D Consistency Optimization for Self-Supervised Monocular Video Depth Estimation

2026-06-14 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

A new self-supervised monocular video depth estimation paradigm addresses geometric inconsistencies and cross-frame drift prevalent in existing methods. These prior approaches often treat video frames independently or rely on weak temporal regularization, lacking a holistic 3D scene perception. The proposed solution recasts sequential video depth estimation as an unconstrained multi-view 3D reconstruction problem, fully exploiting geometric priors from 3D foundation models. Its core is a 3D consistency optimization framework, driven by three constraints: image-level photometric rendering, explicit world-coordinate geometric alignment, and multi-scale temporal gradient consistency. This unified optimization anchors isolated frames to a globally coherent 3D structure. Validated in self-supervised training and challenging zero-shot clinical environments, the method achieves state-of-the-art spatial accuracy, outperforming frame-based, video-based depth estimators, and multi-view 3D reconstruction baselines, proving crucial for endoscopic navigation and embodied AI.

Key takeaway

For Computer Vision Engineers developing monocular video depth estimation for applications like endoscopic navigation or embodied AI, if you are encountering issues with geometrically inconsistent predictions or cross-frame drift, this research suggests a powerful new approach. You should explore recasting your problem as an unconstrained multi-view 3D reconstruction, integrating 3D consistency optimization driven by photometric rendering, geometric alignment, and temporal gradient consistency. This method offers state-of-the-art spatial accuracy and global 3D coherence.

Key insights

Recasting monocular video depth as multi-view 3D reconstruction with 3D consistency optimization improves geometric accuracy and coherence.

Principles

• Holistic 3D scene perception prevents cross-frame drift.
• Leverage 3D foundation models for powerful geometric priors.
• Unified optimization anchors isolated frames to a globally coherent 3D structure.

Method

The approach recasts sequential video depth estimation as unconstrained multi-view 3D reconstruction, driven by a 3D consistency optimization framework with three specific constraints.

In practice

• Improve 3D reasoning in endoscopic navigation.
• Enhance embodied AI systems with geometrically consistent depth.

Topics

• Monocular Depth Estimation
• 3D Reconstruction
• Self-Supervised Learning
• Geometric Consistency
• Endoscopic Navigation
• Embodied AI

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.