UniDepth: Universal Monocular Metric Depth Estimation

TL;DR

UniDepth is a universal monocular depth estimation model that predicts metric 3D scenes from single images across various domains without additional info, outperforming domain-specific methods in zero-shot tests.

Contribution

UniDepth introduces a novel approach with a self-promptable camera module and pseudo-spherical output, enabling robust zero-shot monocular depth estimation across diverse datasets.

Findings

Outperforms existing methods in zero-shot evaluations

Generalizes well across ten diverse datasets

Does not require domain-specific training data

Abstract

Accurate monocular metric depth estimation (MMDE) is crucial to solving downstream tasks in 3D perception and modeling. However, the remarkable accuracy of recent MMDE methods is confined to their training domains. These methods fail to generalize to unseen domains even in the presence of moderate domain gaps, which hinders their practical applicability. We propose a new model, UniDepth, capable of reconstructing metric 3D scenes from solely single images across domains. Departing from the existing MMDE methods, UniDepth directly predicts metric 3D points from the input image at inference time without any additional information, striving for a universal and flexible MMDE solution. In particular, UniDepth implements a self-promptable camera module predicting dense camera representation to condition depth features. Our model exploits a pseudo-spherical output representation, which disentangles camera and depth representations. In addition, we propose a geometric invariance loss that promotes the invariance of camera-prompted depth features. Thorough evaluations on ten datasets in a zero-shot regime consistently demonstrate the superior performance of UniDepth, even when compared with methods directly trained on the testing domains. Code and models are available at: https://github.com/lpiccinelli-eth/unidepth