Self-Supervised Monocular Depth Hints (1909.09051v1)

Abstract: Monocular depth estimators can be trained with various forms of self-supervision from binocular-stereo data to circumvent the need for high-quality laser scans or other ground-truth data. The disadvantage, however, is that the photometric reprojection losses used with self-supervised learning typically have multiple local minima. These plausible-looking alternatives to ground truth can restrict what a regression network learns, causing it to predict depth maps of limited quality. As one prominent example, depth discontinuities around thin structures are often incorrectly estimated by current state-of-the-art methods. Here, we study the problem of ambiguous reprojections in depth prediction from stereo-based self-supervision, and introduce Depth Hints to alleviate their effects. Depth Hints are complementary depth suggestions obtained from simple off-the-shelf stereo algorithms. These hints enhance an existing photometric loss function, and are used to guide a network to learn better weights. They require no additional data, and are assumed to be right only sometimes. We show that using our Depth Hints gives a substantial boost when training several leading self-supervised-from-stereo models, not just our own. Further, combined with other good practices, we produce state-of-the-art depth predictions on the KITTI benchmark.

• The paper introduces Depth Hints to guide self-supervised monocular depth estimation and mitigate challenges from ambiguous photometric loss landscapes.
• It integrates efficiently computed stereo matching signals selectively to refine network optimization through an added supervised log L1 loss element.
• Results on the KITTI benchmark show improved SqRel and RMSE metrics, benefiting real-time applications in AR, robotics, and autonomous driving.

Self-Supervised Monocular Depth Hints

The paper "Self-Supervised Monocular Depth Hints" offers an innovative approach to enhancing monocular depth estimation by incorporating `Depth Hints' derived from traditional stereo algorithms into self-supervised training frameworks. Traditional methods for obtaining depth information often rely on expensive and complex hardware like LiDAR. The paper addresses these challenges by deploying self-supervised learning paradigms, using photometric reprojection losses, but highlights the significant drawbacks associated with multiple local minima in such losses. These local minima can result in suboptimal depth predictions, typically failing at depth discontinuities and thin structures.

Key Contributions

The research introduces the concept of Depth Hints to navigate and mitigate the limitations imposed by ambiguous reprojected losses inherent in traditional stereo self-supervision. These hints act as intermittent depth suggestions, derived from common stereo matching algorithms such as Semi-Global Matching (SGM), which are computationally efficient and can be executed with hyperparameter variations to create diverse depth maps. The pivotal assertion here is that these hints are effective even if correct only intermittently, as the model leverages them to escape local minima traps during training, thereby producing improved depth predictions.

Methodology

The methodology centers around augmenting existing self-supervised photometric loss functions with Depth Hints. The paper defines a structured condition under which these hints are integrated: hints are considered only when they yield a more favorable reprojection loss than current estimates. Should the hint prove advantageous in minimizing the loss, it incorporates a supervised loss element (log L1) alongside the existing self-supervised loss. This selective assimilation is designed to refine and guide network weight optimization towards better minima within the training landscape.

Results and Performance

Quantitatively, the integration of Depth Hints demonstrates an enhancement in depth estimation accuracy across various self-supervised models, noticeably outperforming methods devoid of this augmentation in several key metrics on the KITTI benchmark. Notably, the results exhibit that while standalone proxy-supervised methods using SGM hints formed a robust baseline, the comprehensive model with Depth Hints further refined predictions, surpassing leading benchmarks across multiple categories. Improvements were particularly significant in metrics like squared relative error (SqRel) and RMSE, which are sensitive to larger errors typically introduced by occlusion and intricate structures.

Theoretical Implications

The addition of Depth Hints provides a novel perspective on the potential of leveraging auxiliary information to guide self-supervised models, highlighting that state-of-the-art results can be achieved without traditional supervision. This paves the way for further investigation into similar methodologies that can incorporate alternative auxiliary signals, potentially from other heuristic-driven algorithms.

Practical Implications

In practical terms, the enhanced depth maps can greatly benefit augmented reality, robotics, and autonomous driving, where accurate scene understanding plays a critical role. By reducing dependency on expensive depth sensors, the proposed methodology also brings depth estimation capabilities closer to real-time applications on consumer-grade hardware.

Future Directions

The paper alludes to prospective avenues that could extend the application of Depth Hints beyond stereo-based models, particularly into monocular-video scenarios where depth hints for pose estimation could further improve accuracy and generalizability. Additionally, given that current Depth Hints are derived from stereo imagery, future developments might explore the fusion of other sensory data to broaden the hint generation framework.

In conclusion, this paper presents a compelling case for the integration of Depth Hints in self-supervised monocular depth estimation systems, underlining their role in addressing inherent challenges tied to photometric loss landscapes. This approach not only propels the state-of-the-art in depth prediction but also serves as an exemplary instance of efficiently harnessing traditional computer vision algorithms to enhance modern learning-based models.