{"adoption": {"forks": 117, "observed_at": "2026-08-28T04:04:07.200037+00:00", "stars": 1246}, "canonical_url": "https://ross.abutalabs.com/products/unidepth", "card": {"archived": false, "artifact_type": "library", "description": "Universal Monocular Metric Depth Estimation", "domain": ["computer-vision", "machine-learning", "artificial-intelligence", "robotics"], "enriched": true, "function": ["computer-vision", "image-processing", "machine-learning", "deep-learning"], "health_score": 33, "homepage": null, "language": "Python", "license": "NOASSERTION", "license_family": "other", "maturity": "active", "member_repos": ["lpiccinelli-eth/UniDepth"], "name": "lpiccinelli-eth/UniDepth", "platform": ["python"], "pushed_at": "2025-05-18T21:52:52+00:00", "repo": "lpiccinelli-eth/UniDepth", "stars": 1246, "tags": ["depth-estimation", "monocular-depth", "metric-depth", "3d-reconstruction", "research-code", "pytorch", "linux", "gpu"], "topics": ["3d-reconstruction", "computer-vision", "depth-estimation"], "urls": [], "use_cases": ["estimate metric depth from a single image", "3d reconstruction from monocular photos", "depth estimation for robotics or autonomous navigation", "run state-of-the-art depth models on KITTI or NYU Depth", "get point clouds from single camera images"], "what_it_is": "UniDepth is a Python library and research codebase for universal monocular metric depth estimation from single images, based on CVPR 2024 and UniDepthV2 papers from ETH Zurich. It provides pretrained models, inference, and training code that predict metric depth without requiring camera intrinsics.", "when_to_avoid": ["you need real-time depth on low-power or CPU-only hardware", "you need a non-research license for commercial use (license is custom)", "you need stereo or multi-view depth rather than monocular"], "when_to_choose": ["you need metric (not just relative) depth from a single image without camera intrinsics", "you want a research-grade model with pretrained weights and training code", "you work in PyTorch and need benchmark-leading depth estimation"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/unidepth", "repo": "lpiccinelli-eth/UniDepth", "role": "main", "score": 35}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:04:07.200037+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T05:08:04.763904+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "174e8b4c03e71913fe95f0a4c817c2025ea100ea36131c62abac31b95f74e0a6", "fetched_at": "2026-08-28T04:04:07.200037+00:00", "kind": "readme", "missing": false, "url": "https://github.com/lpiccinelli-eth/UniDepth"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:04:07.200037+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T05:08:04.763904+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "174e8b4c03e71913fe95f0a4c817c2025ea100ea36131c62abac31b95f74e0a6", "fetched_at": "2026-08-28T04:04:07.200037+00:00", "kind": "readme", "missing": false, "url": "https://github.com/lpiccinelli-eth/UniDepth"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T05:08:04.763904+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "174e8b4c03e71913fe95f0a4c817c2025ea100ea36131c62abac31b95f74e0a6", "fetched_at": "2026-08-28T04:04:07.200037+00:00", "kind": "readme", "missing": false, "url": "https://github.com/lpiccinelli-eth/UniDepth"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:04:07.200037+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:04:07.200037+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:04:07.200037+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T05:08:04.763904+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "174e8b4c03e71913fe95f0a4c817c2025ea100ea36131c62abac31b95f74e0a6", "fetched_at": "2026-08-28T04:04:07.200037+00:00", "kind": "readme", "missing": false, "url": "https://github.com/lpiccinelli-eth/UniDepth"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:04:07.200037+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:04:07.200037+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T05:08:04.763904+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "174e8b4c03e71913fe95f0a4c817c2025ea100ea36131c62abac31b95f74e0a6", "fetched_at": "2026-08-28T04:04:07.200037+00:00", "kind": "readme", "missing": false, "url": "https://github.com/lpiccinelli-eth/UniDepth"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:04:07.200037+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:04:07.200037+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:04:07.200037+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T05:08:04.763904+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "174e8b4c03e71913fe95f0a4c817c2025ea100ea36131c62abac31b95f74e0a6", "fetched_at": "2026-08-28T04:04:07.200037+00:00", "kind": "readme", "missing": false, "url": "https://github.com/lpiccinelli-eth/UniDepth"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:04:07.200037+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:04:07.200037+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T05:08:04.763904+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "174e8b4c03e71913fe95f0a4c817c2025ea100ea36131c62abac31b95f74e0a6", "fetched_at": "2026-08-28T04:04:07.200037+00:00", "kind": "readme", "missing": false, "url": "https://github.com/lpiccinelli-eth/UniDepth"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T05:08:04.763904+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "174e8b4c03e71913fe95f0a4c817c2025ea100ea36131c62abac31b95f74e0a6", "fetched_at": "2026-08-28T04:04:07.200037+00:00", "kind": "readme", "missing": false, "url": "https://github.com/lpiccinelli-eth/UniDepth"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T05:08:04.763904+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "174e8b4c03e71913fe95f0a4c817c2025ea100ea36131c62abac31b95f74e0a6", "fetched_at": "2026-08-28T04:04:07.200037+00:00", "kind": "readme", "missing": false, "url": "https://github.com/lpiccinelli-eth/UniDepth"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T05:08:04.763904+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "174e8b4c03e71913fe95f0a4c817c2025ea100ea36131c62abac31b95f74e0a6", "fetched_at": "2026-08-28T04:04:07.200037+00:00", "kind": "readme", "missing": false, "url": "https://github.com/lpiccinelli-eth/UniDepth"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 22, "longevity": 63, "rhythm": 35}, "computed_at": "2026-09-03T02:20:16.233290+00:00", "flags": ["no_releases", "no_license"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 891, "days_push": 472, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 35, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}