Enhancing Self-Supervised Monocular Depth Estimation via Piece-Wise Pose Estimation and Geometric Constraints
Bibliographic record
Abstract
Existing single and multi-frame monocular depth estimation (MDE) approaches lack depth estimation consistency around object edges, while single-frame approaches generate scale-ambiguous depth albeit at a lower computational complexity. We revisit the framework design to address these limitations and propose a joint approach that intertwines depth estimation and panoptic segmentation networks. We present an instance-aware patch-based contrastive loss to ensure depth consistency within an object in feature space. This approach disentangles the embedding triplet and independently refines anchor-positive and anchor-negative pairs, providing coherent depth within objects. Leveraging the panoptic information, we propose masking small objects during photometric loss computation while extracting 6-DoF pose estimates for dynamic objects in a piece-wise approach, thus facilitating depth estimation in dynamic scenes. We demonstrate this mechanism to be suited for single and multi-frame MDE. In addition, to ensure scale fidelity in single-frame MDE, we capitalize on the inherent linear relationship between computed depth and ground truth when using self-supervised photometric loss-based MDE. For this, we propose using a multi-frame depth estimation as a teacher network to inject geometric insight into the student MDE via a global scaling factor, thus generating absolute depth. We further improve the teacher network architecture by introducing a multi-scale feature fusion mechanism that benefits scenarios with significant camera motion. We perform a comprehensive evaluation to validate the efficacy of the proposed mechanism and obtain state-of-the-art performance on the KITTI dataset.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".