Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model

• URL: http://arxiv.org/abs/2312.13252v1
• Date: Wed, 20 Dec 2023 18:27:47 GMT
• Title: Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model
• Authors: Saurabh Saxena, Junhwa Hur, Charles Herrmann, Deqing Sun, David J. Fleet
• Abstract summary: Methods for monocular depth estimation have made significant strides on standard benchmarks, but zero-shot metric depth estimation remains unsolved. Recent work has proposed specialized multi-head architectures for jointly modeling indoor and outdoor scenes. We advocate a generic, task-agnostic diffusion model, with several advancements such as log-scale depth parameterization.
• Score: 34.85279074665031
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While methods for monocular depth estimation have made significant strides on standard benchmarks, zero-shot metric depth estimation remains unsolved. Challenges include the joint modeling of indoor and outdoor scenes, which often exhibit significantly different distributions of RGB and depth, and the depth-scale ambiguity due to unknown camera intrinsics. Recent work has proposed specialized multi-head architectures for jointly modeling indoor and outdoor scenes. In contrast, we advocate a generic, task-agnostic diffusion model, with several advancements such as log-scale depth parameterization to enable joint modeling of indoor and outdoor scenes, conditioning on the field-of-view (FOV) to handle scale ambiguity and synthetically augmenting FOV during training to generalize beyond the limited camera intrinsics in training datasets. Furthermore, by employing a more diverse training mixture than is common, and an efficient diffusion parameterization, our method, DMD (Diffusion for Metric Depth) achieves a 25\% reduction in relative error (REL) on zero-shot indoor and 33\% reduction on zero-shot outdoor datasets over the current SOTA using only a small number of denoising steps. For an overview see https://diffusion-vision.github.io/dmd

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