Towards Zero-Shot Scale-Aware Monocular Depth Estimation

• URL: http://arxiv.org/abs/2306.17253v1
• Date: Thu, 29 Jun 2023 18:46:11 GMT
• Title: Towards Zero-Shot Scale-Aware Monocular Depth Estimation
• Abstract summary: We introduce ZeroDepth, a novel monocular depth estimation framework capable of predicting metric scale for arbitrary test images. This is achieved by (i) the use of input-level geometric embeddings that enable the network to learn a scale prior to objects, and (ii) decoupling the encoder and decoder stages.
• Score: 28.42580603643096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Monocular depth estimation is scale-ambiguous, and thus requires scale supervision to produce metric predictions. Even so, the resulting models will be geometry-specific, with learned scales that cannot be directly transferred across domains. Because of that, recent works focus instead on relative depth, eschewing scale in favor of improved up-to-scale zero-shot transfer. In this work we introduce ZeroDepth, a novel monocular depth estimation framework capable of predicting metric scale for arbitrary test images from different domains and camera parameters. This is achieved by (i) the use of input-level geometric embeddings that enable the network to learn a scale prior over objects; and (ii) decoupling the encoder and decoder stages, via a variational latent representation that is conditioned on single frame information. We evaluated ZeroDepth targeting both outdoor (KITTI, DDAD, nuScenes) and indoor (NYUv2) benchmarks, and achieved a new state-of-the-art in both settings using the same pre-trained model, outperforming methods that train on in-domain data and require test-time scaling to produce metric estimates.

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