@inproceedings{
liu2024smdepth,
title={{SM}4Depth: Seamless Monocular Metric Depth Estimation across Multiple Cameras and Scenes by One Mode},
author={Yihao Liu and Feng Xue and Anlong Ming and Mingshuai Zhao and Huadong Ma and Nicu Sebe},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=nsHBUGeZmH}
}
In the last year, universal monocular metric depth estimation (universal MMDE) has gained considerable attention, serving as the foundation model for various multimedia tasks, such as video and image editing. Nonetheless, current approaches face challenges in maintaining consistent accuracy across diverse scenes without scene-specific parameters and pre-training, hindering the practicality of MMDE. Furthermore, these methods rely on extensive datasets comprising millions, if not tens of millions, of data for training, leading to significant time and hardware expenses. This paper presents SM4Depth, a model that seamlessly works for both indoor and outdoor scenes, without needing extensive training data and GPU clusters. Firstly, to obtain consistent depth across diverse scenes, we propose a novel metric scale modeling, i.e., variationbased unnormalized depth bins. It reduces the ambiguity of the conventional metric bins and enables better adaptation to large depth-gaps of scenes during training. Secondly, we propose a “divide and conquer" solution to reduce reliance on massive training data. Instead of estimating directly from the vast solution space, the metric bins are estimated from multiple solution sub-spaces to reduce complexity. Additionally, we introduce an uncut depth dataset, Campus Depth, to evaluate the depth accuracy and consistency across various indoor and outdoor scenes. Trained on a consumer-grade GPU using just 150K RGB-D pairs, SM4Depth achieves outstanding performance on the most never-before-seen datasets, especially maintaining consistent accuracy across indoors and outdoors. The code can be found in the supplementary material.
SM4Depth: Seamless Monocular Metric Depth Estimation across Multiple Cameras and Scenes by One Mode
Yihao Liu Feng Xue Anlong Ming Mingshuai Zhao Huadong Ma Nicu Sebe