On the uncertainty of self-supervised monocular depth estimation (2005.06209v1)

Abstract: Self-supervised paradigms for monocular depth estimation are very appealing since they do not require ground truth annotations at all. Despite the astonishing results yielded by such methodologies, learning to reason about the uncertainty of the estimated depth maps is of paramount importance for practical applications, yet uncharted in the literature. Purposely, we explore for the first time how to estimate the uncertainty for this task and how this affects depth accuracy, proposing a novel peculiar technique specifically designed for self-supervised approaches. On the standard KITTI dataset, we exhaustively assess the performance of each method with different self-supervised paradigms. Such evaluation highlights that our proposal i) always improves depth accuracy significantly and ii) yields state-of-the-art results concerning uncertainty estimation when training on sequences and competitive results uniquely deploying stereo pairs.

• The paper introduces a novel self-teaching paradigm that improves both depth estimation accuracy and uncertainty modeling in self-supervised frameworks.
• It rigorously compares empirical, predictive, and Bayesian methods to evaluate uncertainty on the KITTI dataset across diverse training setups.
• The experimental results demonstrate that combining monocular and stereo supervision yields performance competitive with state-of-the-art techniques.

On the Uncertainty of Self-Supervised Monocular Depth Estimation

The paper "On the Uncertainty of Self-Supervised Monocular Depth Estimation" addresses the challenge of estimating uncertainty in self-supervised frameworks for monocular depth estimation. The motivation arises from the growing importance of monocular depth estimation in applications such as autonomous driving and augmented reality, where understanding the reliability of depth predictions is crucial. The authors contribute by formulating a novel approach to model uncertainty in a self-supervised learning framework, engaging in an exhaustive evaluation of various methodologies to improve depth accuracy and uncertainty estimation.

Key Contributions

The paper's contributions are centered on three primary aspects:

1. Comprehensive Evaluation: The authors conduct an extensive evaluation of existing uncertainty estimation techniques, specifically tailored for self-supervised monocular depth estimation. They distinguish between empirical, predictive, and Bayesian strategies, providing a thorough analysis of their applicability and effectiveness in this context.
2. Self-Teaching Paradigm: The authors introduce a novel Self-Teaching paradigm that models uncertainty by training a student network to mimic the output distribution of a pre-trained teacher network. This approach enhances the depth estimation accuracy and provides a more robust estimate of uncertainty when the pose is unknown.
3. Experimental Validation: Using the standard KITTI dataset, the authors validate their approach across different training paradigms—including monocular, stereo, and combined monocular-stereo supervision—demonstrating significant improvements in both depth accuracy and uncertainty estimation.

Methodology

The investigation into uncertainty estimation incorporates several approaches:

• Empirical Methods: Techniques such as dropout sampling, bootstrapped ensembles, and snapshot ensembles are examined to model uncertainty by analyzing variance across multiple model predictions.
• Predictive Methods: These methods involve training the network to predict both the mean and variance of depth distributions, using a learned reprojection approach and log-likelihood maximization.
• Bayesian Approximations: Combining empirical and predictive strategies to marginalize over model uncertainties, yielding more comprehensive uncertainty assessments.

Results and Analysis

The results highlight several key findings:

• For monocular supervision, the Self-Teaching method notably enhances depth accuracy and provides superior uncertainty estimation compared to other methods. It effectively decouples depth estimation from pose estimation, mitigating the challenges posed by the ill-posed nature of monocular depth estimation.
• With stereo supervision, where the pose is known, predictive methods like log-likelihood maximization perform well, reflecting the utility of incorporating geometric constraints.
• The combination of monocular and stereo supervision further underscores the robustness of the Self-Teaching approach, achieving competitive results with state-of-the-art techniques.

Overall, the proposed methods enrich the predictive capabilities of self-supervised depth estimation models, especially in scenarios where depth and pose uncertainties are prominent challenges.

Implications and Future Directions

This research has both practical and theoretical implications. Practically, improved uncertainty estimation enhances the reliability of depth predictions, essential for safety-critical applications like autonomous navigation. Theoretically, the work advances the understanding of how self-supervised learning paradigms can be adapted to incorporate uncertainty, paving the way for further innovations in self-supervised learning.

Future developments could involve deeper exploration of uncertainty quantification frameworks, the integration of semantic information for richer scene understanding, and extending the methodologies to other self-supervised learning tasks. The potential for applying these insights to various AI applications underscores the broad relevance of this research.

In summary, the authors provide a significant contribution to the field of monocular depth estimation by addressing the often overlooked aspect of uncertainty estimation, incorporating novel techniques that demonstrate efficacy across diverse evaluation conditions.