GRIN: Zero-Shot Metric Depth with Pixel-Level Diffusion
This addresses the challenge of accurate depth estimation in real-world settings where dense ground-truth data is often unavailable, though it is incremental as it builds on existing diffusion model approaches.
The paper tackles the problem of 3D reconstruction from a single image by introducing GRIN, an efficient diffusion model that ingests sparse unstructured training data, establishing a new state of the art in zero-shot metric monocular depth estimation across eight indoor and outdoor datasets.
3D reconstruction from a single image is a long-standing problem in computer vision. Learning-based methods address its inherent scale ambiguity by leveraging increasingly large labeled and unlabeled datasets, to produce geometric priors capable of generating accurate predictions across domains. As a result, state of the art approaches show impressive performance in zero-shot relative and metric depth estimation. Recently, diffusion models have exhibited remarkable scalability and generalizable properties in their learned representations. However, because these models repurpose tools originally designed for image generation, they can only operate on dense ground-truth, which is not available for most depth labels, especially in real-world settings. In this paper we present GRIN, an efficient diffusion model designed to ingest sparse unstructured training data. We use image features with 3D geometric positional encodings to condition the diffusion process both globally and locally, generating depth predictions at a pixel-level. With comprehensive experiments across eight indoor and outdoor datasets, we show that GRIN establishes a new state of the art in zero-shot metric monocular depth estimation even when trained from scratch.