TikTok has introduced a groundbreaking development in Monocular Depth Estimation (MDE) with the release of “Depth Anything.” This innovative model leverages a colossal dataset, consisting of 62 million images, to establish itself as a foundational model in the field. Unlike traditional approaches, Depth Anything focuses on simplicity and power, setting new standards for robust image-based depth estimation.
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The Power of Large-scale Unlabeled Data
Depth Anything relies on a dataset comprising 1.5 million labeled images and an impressive 62 million unlabeled images. This extensive dataset expansion is achieved through a data engine designed for collecting and automatically annotating unlabeled data. The key to its success lies in the significant reduction of generalization errors, making it a practical solution for monocular depth estimation.
Strategies for Success
The model employs two effective strategies to enhance its capabilities. Firstly, a more challenging optimization target is created using data augmentation tools, compelling the model to actively seek additional visual knowledge. Secondly, auxiliary supervision ensures the model inherits rich semantic priors from pre-trained encoders, enhancing its ability to interpret and understand diverse images.
Setting New Benchmarks
In zero-shot evaluations across six public datasets and random photos, Depth Anything outperforms its predecessors. Notably, it achieves superior zero-shot relative depth estimation and metric depth estimation compared to MiDaS v3.1 and ZoeDepth, respectively. Fine-tuning on NYUv2 and KITTI datasets establishes new State-of-the-Art benchmarks, showcasing its versatility and prowess in monocular depth estimation.
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Beyond Depth: Enhancing ControlNet
The impact of Depth Anything extends beyond depth estimation. Researchers re-trained a depth-conditioned ControlNet based on this model, surpassing the previous version dependent on MiDaS. This enhancement indicates broader applicability in areas like autonomous driving, emphasizing the model’s role in understanding complex environments.
Visualizing Depth in Dynamic Scenarios
Despite being primarily image-based, Depth Anything’s capabilities extend to dynamic scenarios showcased through video demonstrations. These visualizations underscore the model’s superiority in real-world situations, offering a glimpse into its potential applications.
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Our Say
TikTok’s Depth Anything marks a significant stride in AI-driven depth perception, revolutionizing monocular depth estimation. Its reliance on a massive dataset, coupled with effective strategies and impressive benchmarks, positions it as a robust foundational model. The model’s simplicity and power, combined with its applicability beyond depth estimation, make it a noteworthy advancement in the field. Depth Anything exemplifies the potential unlocked through scaled-up diverse training data, showcasing TikTok’s commitment to innovation in AI research.
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