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Prompting Depth Anything: A New Paradigm for High-Resolution Metric Depth Estimation

A groundbreaking new technique, “Prompt Depth Anything,” has been introduced, revolutionizing metric depth estimation—the process of accurately determining the distance of objects in images. Published in a recent arXiv paper, this method leverages the power of “prompting,” a technique where additional information guides a foundation model to perform a specific task, in this case, depth estimation. Unlike previous methods that often struggle with scale ambiguity and accuracy, Prompt Depth Anything achieves high-resolution (up to 4K) and accurate metric depth maps.

The Power of Prompting:

Traditional monocular depth estimation, using just a single camera, has made significant strides with the development of depth foundation models. These models, pre-trained on massive datasets, excel at creating relative depth maps, showing which objects are closer or further than others. However, they frequently fail to provide accurate metric depth—the actual distance in meters or centimeters. Prompt Depth Anything addresses this limitation. It uses a low-cost LiDAR sensor as a “prompt,” providing the model with precise distance information. This acts as a guide, helping the model refine its initial depth estimations and produce accurate, scaled output.

Concrete Examples:

Imagine trying to judge the distance of cars in a photo of a road. A standard depth foundation model might correctly identify the relative distances (car A is closer than car B), but its estimates could be wildly inaccurate in terms of actual meters. Prompt Depth Anything, by incorporating LiDAR data (which provides absolute distances), ensures that the model’s output reflects the actual distances to the cars in the image.

Similarly, in robotics, accurate depth information is crucial for safe and effective grasping. Imagine a robot trying to pick up an object. A system relying solely on a depth foundation model might misjudge the object’s position due to scale inaccuracies. With Prompt Depth Anything, the LiDAR prompt ensures the robot has the precise distance data needed for successful grasping.

Data Challenges and Solutions:

Training such a model requires a dataset containing both high-quality depth information from a LiDAR and ground truth depth. Such paired data is scarce, and existing datasets often have inaccurate ground truth or missing LiDAR depth. The researchers overcame this challenge by creating a scalable data pipeline. This pipeline uses synthetic data simulation to generate realistic LiDAR data paired with precise ground truth and processes real-world data using a technique called 3D reconstruction with a novel edge-aware loss function to improve the accuracy of pseudo ground-truth depth.

State-of-the-Art Results:

The researchers evaluated Prompt Depth Anything on two benchmark datasets, ARKitScenes and ScanNet++, and consistently achieved state-of-the-art results, outperforming existing methods in both accuracy and resolution. The model’s ability to generalize to unseen scenarios and its application in 3D reconstruction and robotic grasping highlight its potential impact across various fields.

Future Directions:

While the paper demonstrates the remarkable performance of Prompt Depth Anything, the authors also acknowledge limitations. The system’s reliance on LiDAR for the prompt means that longer-range depth estimation might be less accurate, and temporal inconsistencies in LiDAR data can affect the model’s output. Future research will focus on addressing these issues, potentially by exploring more sophisticated prompting techniques and improvements to the data pipeline. Regardless, the introduction of “prompting” in depth estimation is a significant advancement, setting a new standard for high-accuracy, high-resolution metric depth estimation.