{"work":{"id":"0b67883b-1901-45f1-9d58-1ef7a928df23","openalex_id":"https://openalex.org/W4403882324","doi":"10.48550/arxiv.2410.02073","arxiv_id":"2410.02073","raw_key":null,"title":"Depth Pro: Sharp Monocular Metric Depth in Less Than a Second","authors":null,"authors_text":"Aleksei Bochkovskii, Ama\\\"el Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R. Richter","year":2024,"venue":"cs.CV","abstract":"We present a foundation model for zero-shot metric monocular depth estimation. Our model, Depth Pro, synthesizes high-resolution depth maps with unparalleled sharpness and high-frequency details. The predictions are metric, with absolute scale, without relying on the availability of metadata such as camera intrinsics. And the model is fast, producing a 2.25-megapixel depth map in 0.3 seconds on a standard GPU. These characteristics are enabled by a number of technical contributions, including an efficient multi-scale vision transformer for dense prediction, a training protocol that combines real and synthetic datasets to achieve high metric accuracy alongside fine boundary tracing, dedicated evaluation metrics for boundary accuracy in estimated depth maps, and state-of-the-art focal length estimation from a single image. Extensive experiments analyze specific design choices and demonstrate that Depth Pro outperforms prior work along multiple dimensions. We release code and weights at https://github.com/apple/ml-depth-pro","external_url":"https://arxiv.org/abs/2410.02073","cited_by_count":17,"metadata_source":"pith","metadata_fetched_at":"2026-08-05T02:28:24.338817+00:00","pith_arxiv_id":"2410.02073","created_at":"2026-05-08T21:34:15.170135+00:00","updated_at":"2026-08-05T02:28:24.338817+00:00","title_quality_ok":true,"display_title":"Depth Pro: Sharp Monocular Metric Depth in Less Than a Second","render_title":"Depth Pro: Sharp Monocular Metric Depth in Less Than a Second"},"hub":{"state":{"work_id":"0b67883b-1901-45f1-9d58-1ef7a928df23","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":60,"external_cited_by_count":17,"distinct_field_count":4,"first_pith_cited_at":"2024-11-21T16:41:55+00:00","last_pith_cited_at":"2026-07-09T17:59:58+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-08-23T02:59:20.602165+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":4},{"context_role":"baseline","n":2},{"context_role":"method","n":2}],"polarity_counts":[{"context_polarity":"background","n":3},{"context_polarity":"use_method","n":2},{"context_polarity":"baseline","n":1},{"context_polarity":"contest","n":1},{"context_polarity":"unclear","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}