Depth Anything: A Robust Monocular Depth Estimation Model Leveraging Large-Scale Unlabeled Data

Kernkonzepte

This work presents Depth Anything, a highly practical solution for robust monocular depth estimation. By scaling up the dataset with large-scale unlabeled data and employing effective training strategies, the model exhibits impressive generalization ability across extensive unseen scenes.

Zusammenfassung

This work presents Depth Anything, a practical solution for robust monocular depth estimation. The key highlights are:

1. Data scaling-up with large-scale unlabeled data:

• Collected 62M diverse and informative unlabeled images from various public datasets.
• Automatically annotated the unlabeled images using a pre-trained depth estimation model.
• Jointly trained the model on both labeled and pseudo-labeled data.

2. Effective training strategies:

• Challenged the student model with a more difficult optimization target when learning unlabeled images, compelling it to seek extra visual knowledge and acquire robust representations.
• Enforced the model to inherit rich semantic priors from a pre-trained encoder via a feature alignment loss, enhancing its scene understanding capability.

The resulting Depth Anything model exhibits impressive zero-shot depth estimation performance on six unseen datasets, outperforming the state-of-the-art MiDaS model. Further, when fine-tuned with metric depth information, it sets new state-of-the-art results. The trained encoder also serves as a strong multi-task foundation for downstream applications like semantic segmentation.

Statistiken

The model is trained on a total of 1.5M labeled images and 62M unlabeled images.

Zitate

"We highlight the value of data scaling-up of massive, cheap, and diverse unlabeled images for MDE."
"We point out a key practice in jointly training large-scale labeled and unlabeled images. Instead of learning raw unlabeled images directly, we challenge the model with a harder optimization target for extra knowledge."
"We propose to inherit rich semantic priors from pre-trained encoders for better scene understanding, rather than using an auxiliary semantic segmentation task."

Wichtige Erkenntnisse aus

Depth Anything

by Lihe Yang,Bi... um arxiv.org 04-09-2024

https://arxiv.org/pdf/2401.10891.pdf

Tiefere Fragen

How can the proposed strategies be extended to other computer vision tasks beyond monocular depth estimation

The strategies proposed in the Depth Anything paper can be extended to various other computer vision tasks beyond monocular depth estimation. One way to do this is by applying the concept of data scaling-up using large-scale unlabeled data to tasks like semantic segmentation, object detection, and image classification. By collecting diverse and informative unlabeled images and leveraging them to enhance the model's generalization ability, similar improvements can be achieved in these tasks as well. Additionally, the idea of challenging the model with a more difficult optimization target and enforcing auxiliary supervision can be adapted to different tasks to improve performance and robustness. For instance, in object detection, the model can be challenged with more complex backgrounds or occlusions, while in semantic segmentation, the model can be guided to learn rich semantic priors from pre-trained encoders to enhance scene understanding.

What are the potential limitations of relying on large-scale unlabeled data, and how can they be addressed

While relying on large-scale unlabeled data offers significant advantages in terms of data coverage and model generalization, there are potential limitations that need to be addressed. One limitation is the quality of the pseudo labels generated for the unlabeled data, which may introduce noise and affect the model's performance. This can be mitigated by incorporating stronger perturbations during training to help the model learn robust representations and handle noisy labels effectively. Another limitation is the computational resources required to process and train on such a large amount of data. To address this, techniques like distributed training, data parallelism, and efficient data processing pipelines can be implemented to optimize resource utilization and training efficiency. Additionally, ensuring the diversity and representativeness of the unlabeled data is crucial to prevent bias and ensure the model's ability to generalize across different scenarios.

Given the strong multi-task capabilities of the trained encoder, how can it be leveraged to enable efficient and versatile visual perception systems

The trained encoder, with its strong multi-task capabilities, can be leveraged to enable efficient and versatile visual perception systems in various ways. One approach is to use the encoder as a feature extractor for downstream tasks such as object detection, image segmentation, and scene understanding. By fine-tuning the encoder on specific tasks and leveraging its learned representations, models can benefit from the encoder's rich semantic information and robust features. Additionally, the encoder can serve as a universal backbone for different visual perception tasks, allowing for transfer learning and multi-task learning scenarios. This can lead to more efficient and effective models that can perform multiple tasks with a single shared encoder, reducing redundancy and improving overall system performance.