SM4Depth: Seamless Monocular Metric Depth Estimation across Multiple Cameras and Scenes by One Model

• URL: http://arxiv.org/abs/2403.08556v1
• Date: Wed, 13 Mar 2024 14:08:25 GMT
• Title: SM4Depth: Seamless Monocular Metric Depth Estimation across Multiple Cameras and Scenes by One Model
• Authors: Yihao Liu and Feng Xue and Anlong Ming
• Abstract summary: This paper proposes SM4Depth, a seamless MMDE method to address all the issues above within a single network. First, we reveal that a consistent field of view (FOV) is the key to resolve metric ambiguity'' across cameras. Second, to achieve consistently high accuracy across scenes, we explicitly model the metric scale determination as discretizing the depth interval into bins. Third, to reduce the reliance on massive training data, we propose a divide and conquer" solution.
• Score: 23.95095404136943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The generalization of monocular metric depth estimation (MMDE) has been a longstanding challenge. Recent methods made progress by combining relative and metric depth or aligning input image focal length. However, they are still beset by challenges in camera, scene, and data levels: (1) Sensitivity to different cameras; (2) Inconsistent accuracy across scenes; (3) Reliance on massive training data. This paper proposes SM4Depth, a seamless MMDE method, to address all the issues above within a single network. First, we reveal that a consistent field of view (FOV) is the key to resolve ``metric ambiguity'' across cameras, which guides us to propose a more straightforward preprocessing unit. Second, to achieve consistently high accuracy across scenes, we explicitly model the metric scale determination as discretizing the depth interval into bins and propose variation-based unnormalized depth bins. This method bridges the depth gap of diverse scenes by reducing the ambiguity of the conventional metric bin. Third, to reduce the reliance on massive training data, we propose a ``divide and conquer" solution. Instead of estimating directly from the vast solution space, the correct metric bins are estimated from multiple solution sub-spaces for complexity reduction. Finally, with just 150K RGB-D pairs and a consumer-grade GPU for training, SM4Depth achieves state-of-the-art performance on most previously unseen datasets, especially surpassing ZoeDepth and Metric3D on mRI$_\theta$. The code can be found at https://github.com/1hao-Liu/SM4Depth.

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