toplogo
Sign In

Transferring Depth-Scale from Labeled to Unlabeled Domains Using Self-Supervised Depth Estimation


Core Concepts
A method to transfer the depth-scale from source datasets with ground-truth depth labels to target datasets without depth measurements, by leveraging the linear relationship between predicted up-to-scale depths and their ground-truth values.
Abstract
The paper presents a novel method for transferring the depth-scale from source datasets with ground-truth (GT) depth labels to target datasets without any depth measurements. The key insights are: Self-supervised depth estimators result in up-to-scale depth predictions that are linearly correlated to their absolute GT depth values across the domain. This linear relationship can be modeled using a single scalar factor. Aligning the field-of-view (FOV) of the source and target datasets prior to training results in a shared linear depth ranking scale between the domains. The method first trains the depth network using self-supervision on a mix of source and target images (with FOV alignment). It then estimates the depth-scale factor by fitting a linear model between the source up-to-scale predictions and their GT depths. Finally, this factor is used to scale the target up-to-scale predictions, achieving absolute depth estimates on the new domain. The proposed method was successfully demonstrated on the KITTI, DDAD and nuScenes datasets, using various existing real or synthetic source datasets, achieving comparable or better accuracy than other depth-scale transfer methods that do not use target GT depths.
Stats
The predicted up-to-scale depths are linearly correlated to their ground-truth depth values, with a Pearson correlation coefficient above 0.76. After filtering out poor up-to-scale predictions, the Pearson correlation coefficient increases to above 0.97. The linear depth ranking scale factor (Gdscale) calculated on the source dataset can be effectively transferred to the target dataset when training on a mix of source and target images.
Quotes
"We show that although such models can predict only up-to-scale depths, these are linearly correlated with their respective GT depths, not only per a single image, but also across multiple images, displaying linear correlation characteristics per dataset, a property which we refer to in this work as linear depth ranking." "Moreover, we show that when adjusting images from two different domains to a single FOV, under the assumption of similar camera heights, training the MDE on images from both domains results in a shared depth ranking scale, regardless of possible domain gaps."

Deeper Inquiries

How would the proposed depth-scale transfer method perform on datasets with significantly different camera heights or sensor types compared to the source domain

The proposed depth-scale transfer method may face challenges when applied to datasets with significantly different camera heights or sensor types compared to the source domain. In such cases, the linear depth ranking assumption may not hold true due to variations in the scene geometry, perspective, and intrinsic camera parameters. The method relies on the assumption of a consistent linear relationship between predicted up-to-scale depths and ground truth depths across images in a dataset. When datasets have different camera heights or sensor types, these assumptions may not be valid, leading to inaccuracies in the depth-scale transfer process. Adjusting for such variations would require additional considerations and potentially more complex modeling to account for the differences in camera heights and sensor characteristics between domains.

What are the limitations of the linear depth ranking assumption, and how could it be extended to handle more complex depth-scale relationships

The linear depth ranking assumption has certain limitations that need to be addressed for more robust depth-scale transfer. One limitation is the assumption of a uniform linear relationship between predicted up-to-scale depths and ground truth depths across all images in a dataset. In reality, this linear relationship may vary due to scene complexity, occlusions, lighting conditions, and other factors. To handle more complex depth-scale relationships, the method could be extended by incorporating non-linear transformations or adaptive scaling factors based on scene characteristics. Additionally, introducing contextual information, such as semantic cues or geometric constraints, could enhance the depth-scale transfer process by capturing more nuanced depth variations in different regions of the scene. By incorporating these enhancements, the method could improve its ability to accurately transfer depth-scale across diverse datasets with varying scene complexities and camera configurations.

Could the depth-scale transfer approach be integrated with other self-supervised depth estimation techniques, such as those leveraging semantic or geometric cues, to further improve the overall depth prediction accuracy

The depth-scale transfer approach could be integrated with other self-supervised depth estimation techniques to enhance overall depth prediction accuracy. By combining the depth-scale transfer method with techniques that leverage semantic or geometric cues, the model can benefit from additional contextual information to improve depth estimation in challenging scenarios. Semantic cues, such as object boundaries or scene semantics, can provide valuable information for refining depth predictions and enhancing depth-scale transfer between domains. Geometric cues, such as scene structure or object relationships, can help constrain depth predictions and improve the consistency of depth-scale transfer across different datasets. By integrating these complementary techniques, the model can leverage a richer set of features and constraints to enhance depth estimation accuracy and robustness in diverse real-world scenarios.
0