HEADLINE: Prompt Depth Anything: 4K Resolution Metric Depth Estimation Using iPhone LiDAR Prompts

In a significant leap for depth estimation, the team behind Prompt Depth Anything has introduced a method that leverages prompting to achieve high-resolution and accurate metric depth maps. This approach, inspired by the success of prompting in vision-language models (VLMs) and large language models (LLMs), uses an iPhone LiDAR as the prompt to guide a foundation model for up to 4K resolution depth estimation.

Key Highlights:

• High-Resolution Depth Estimation: Achieves up to 4K resolution metric depth.
• Prompting with LiDAR: Uses low-cost iPhone LiDAR as a prompt to enhance accuracy.
• Scalable Data Pipeline: Introduces a data pipeline for training, including synthetic data and real data pseudo ground truth (GT) generation.
• Enhanced Downstream Applications: Benefits 3D reconstruction and generalized robotic grasping.

Technical Breakdown

Prompt Fusion Design

The core innovation lies in the prompt fusion design. The Depth Anything model integrates LiDAR data at multiple scales within the depth decoder. This multi-scale integration ensures that the model can leverage the detailed and accurate depth information provided by the LiDAR, even at high resolutions.

• Multi-Scale Integration: The LiDAR data is fused with the model's internal representations at various stages of the decoding process.
• Concise Prompt Fusion: The design is kept concise to maintain computational efficiency while ensuring that the LiDAR information is effectively utilized.

Scalable Data Pipeline

Training a depth estimation model requires large and diverse datasets. To address this, the team developed a scalable data pipeline:

• Synthetic Data Generation: Simulates LiDAR data for synthetic environments to augment the training set.
• Real Data Pseudo GT Depth: Generates pseudo ground truth depth maps from real-world data using advanced techniques.

This pipeline ensures that the model can be trained on a wide range of scenarios, improving its generalization and robustness.

Performance Benchmarks

Prompt Depth Anything sets new state-of-the-art results on the ARKitScenes and ScanNet++ datasets:

• ARKitScenes: Demonstrates superior performance in both accuracy and resolution.
• ScanNet++: Achieves high precision in metric depth estimation, outperforming existing methods.

Comparisons with Monocular Depth Methods

Monocular depth methods can generate high-resolution depth maps but often struggle with consistent metric scale information. Even after aligning with LiDAR data, these methods may still lack the accuracy and consistency provided by Prompt Depth Anything.

• Metric Scale Information: Monocular methods often fail to provide accurate metric scale information.
• Consistency: Prompt Depth Anything ensures better consistency in depth estimation across different scenes.

Comparisons with ARKit LiDAR Depth

The ARKit LiDAR provides high-quality depth data but is limited by its hardware capabilities. Prompt Depth Anything enhances this data, achieving higher resolution and more accurate metric depth:

• Resolution: Achieves up to 4K resolution.
• Accuracy: Provides more precise depth information compared to raw ARKit LiDAR data.

Downstream Applications

The high-resolution and accurate metric depth maps generated by Prompt Depth Anything have significant implications for various applications:

• 3D Reconstruction: Enhanced accuracy in 3D models, crucial for virtual reality (VR) and augmented reality (AR) applications.
• Generalized Robotic Grasping: Improved precision in robotic grasping tasks, enabling robots to handle a wider range of objects more effectively.

Conclusion

Prompt Depth Anything represents a significant advancement in depth estimation by leveraging the power of prompting with low-cost LiDAR data. The method's high-resolution and accurate metric depth maps open up new possibilities for 3D reconstruction and robotic applications, making it a valuable tool for researchers and practitioners alike.