# Adaptive confidence thresholding for monocular depth estimation

arXiv: https://arxiv.org/abs/2009.12840 · HTML: https://mlsanity.com/paper/2009.12840/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2020.

## Built on by (most cited first, 25 of 25)

- [MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer](https://mlsanity.com/paper/2208.03543/) (2022)
- [FeatUp: A Model-Agnostic Framework for Features at Any Resolution](https://mlsanity.com/paper/2403.10516/) (2024)
- [Diffusion Models for Monocular Depth Estimation: Overcoming Challenging Conditions](https://mlsanity.com/paper/2407.16698/) (2024)
- [ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization](https://mlsanity.com/paper/2208.08631/) (2022)
- [Self-distilled Feature Aggregation for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2209.07088/) (2022)
- [AugUndo: Scaling Up Augmentations for Monocular Depth Completion and Estimation](https://mlsanity.com/paper/2310.09739/) (2023)
- [Semi-Supervised Learning with Mutual Distillation for Monocular Depth Estimation](https://mlsanity.com/paper/2203.09737/) (2022)
- [The Third Monocular Depth Estimation Challenge](https://mlsanity.com/paper/2404.16831/) (2024)
- [Extending Foundational Monocular Depth Estimators to Fisheye Cameras with Calibration Tokens](https://mlsanity.com/paper/2508.04928/) (2025)
- [Two-in-One Depth: Bridging the Gap Between Monocular and Binocular Self-supervised Depth Estimation](https://mlsanity.com/paper/2309.00933/) (2023)
- [The Fourth Monocular Depth Estimation Challenge](https://mlsanity.com/paper/2504.17787/) (2025)
- [Hybrid-TTA: Continual Test-time Adaptation via Dynamic Domain Shift Detection](https://mlsanity.com/paper/2409.08566/) (2024)
- [SG-MIM: Structured Knowledge Guided Efficient Pre-training for Dense Prediction](https://mlsanity.com/paper/2409.02513/) (2024)
- [UniTT-Stereo: Unified Training of Transformer for Enhanced Stereo Matching](https://mlsanity.com/paper/2409.02545/) (2024)
- [iConFormer: Dynamic Parameter-Efficient Tuning with Input-Conditioned Adaptation](https://mlsanity.com/paper/2409.02838/) (2024)
- [Depth Completion with Multiple Balanced Bases and Confidence for Dense Monocular SLAM](https://mlsanity.com/paper/2309.04145/) (2023)
- [ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimation](https://mlsanity.com/paper/2309.14744/) (2023)
- [A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation](https://mlsanity.com/paper/2501.08188/) (2025)
- [Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation](https://mlsanity.com/paper/2307.09929/) (2023)
- [MaDis-Stereo: Enhanced Stereo Matching via Distilled Masked Image Modeling](https://mlsanity.com/paper/2409.02846/) (2024)
- [MaskingDepth: Masked Consistency Regularization for Semi-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2212.10806/) (2022)
- [Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency](https://mlsanity.com/paper/2401.12019/) (2024)
- [Collaborative Learning for Enhanced Unsupervised Domain Adaptation](https://mlsanity.com/paper/2409.02699/) (2024)
- [Adaptive Depth-converted-Scale Convolution for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2604.07665/) (2026)
- [Geometric Distillation from Rectified Stereo: Leveraging Epipolar Cues for Monocular Depth](https://mlsanity.com/paper/2607.15600/) (2026)

## Abstract (arXiv)

Self-supervised monocular depth estimation has become an appealing solution to the lack of ground truth labels, but its reconstruction loss often produces over-smoothed results across object boundaries and is incapable of handling occlusion explicitly. In this paper, we propose a new approach to leverage pseudo ground truth depth maps of stereo images generated from self-supervised stereo matching methods. The confidence map of the pseudo ground truth depth map is estimated to mitigate performance degeneration by inaccurate pseudo depth maps. To cope with the prediction error of the confidence map itself, we also leverage the threshold network that learns the threshold dynamically conditioned on the pseudo depth maps. The pseudo depth labels filtered out by the thresholded confidence map are used to supervise the monocular depth network. Furthermore, we propose the probabilistic framework that refines the monocular depth map with the help of its uncertainty map through the pixel-adaptive convolution (PAC) layer. Experimental results demonstrate superior performance to state-of-the-art monocular depth estimation methods. Lastly, we exhibit that the proposed threshold learning can also be used to improve the performance of existing confidence estimation approaches.