{"id":47979348,"url":"https://github.com/ByteDance-Seed/Depth-Anything-3","last_synced_at":"2026-04-19T19:00:44.519Z","repository":{"id":324147223,"uuid":"1094841344","full_name":"ByteDance-Seed/Depth-Anything-3","owner":"ByteDance-Seed","description":"Depth Anything 3","archived":false,"fork":false,"pushed_at":"2026-03-21T07:14:45.000Z","size":23215,"stargazers_count":4800,"open_issues_count":175,"forks_count":494,"subscribers_count":43,"default_branch":"main","last_synced_at":"2026-03-26T11:51:08.147Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://depth-anything-3.github.io/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ByteDance-Seed.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-11-12T08:44:03.000Z","updated_at":"2026-03-26T09:48:15.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/ByteDance-Seed/Depth-Anything-3","commit_stats":null,"previous_names":["bytedance-seed/depth-anything-3"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ByteDance-Seed/Depth-Anything-3","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ByteDance-Seed%2FDepth-Anything-3","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ByteDance-Seed%2FDepth-Anything-3/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ByteDance-Seed%2FDepth-Anything-3/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ByteDance-Seed%2FDepth-Anything-3/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ByteDance-Seed","download_url":"https://codeload.github.com/ByteDance-Seed/Depth-Anything-3/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ByteDance-Seed%2FDepth-Anything-3/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32018764,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-18T20:23:30.271Z","status":"online","status_checked_at":"2026-04-19T02:00:07.110Z","response_time":55,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2026-04-04T11:01:01.285Z","updated_at":"2026-04-19T19:00:44.511Z","avatar_url":"https://github.com/ByteDance-Seed.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"\u003cdiv align=\"center\"\u003e\n\u003ch1 style=\"border-bottom: none; margin-bottom: 0px \"\u003eDepth Anything 3: Recovering the Visual Space from Any Views\u003c/h1\u003e\n\u003c!-- \u003ch2 style=\"border-top: none; margin-top: 3px;\"\u003eRecovering the Visual Space from Any Views\u003c/h2\u003e --\u003e\n\n\n[**Haotong Lin**](https://haotongl.github.io/)\u003csup\u003e\u0026ast;\u003c/sup\u003e · [**Sili Chen**](https://github.com/SiliChen321)\u003csup\u003e\u0026ast;\u003c/sup\u003e · [**Jun Hao Liew**](https://liewjunhao.github.io/)\u003csup\u003e\u0026ast;\u003c/sup\u003e · [**Donny Y. Chen**](https://donydchen.github.io)\u003csup\u003e\u0026ast;\u003c/sup\u003e · [**Zhenyu Li**](https://zhyever.github.io/) · [**Guang Shi**](https://scholar.google.com/citations?user=MjXxWbUAAAAJ\u0026hl=en) · [**Jiashi Feng**](https://scholar.google.com.sg/citations?user=Q8iay0gAAAAJ\u0026hl=en)\n\u003cbr\u003e\n[**Bingyi Kang**](https://bingyikang.com/)\u003csup\u003e\u0026ast;\u0026dagger;\u003c/sup\u003e\n\n\u0026dagger;project lead\u0026emsp;\u0026ast;Equal Contribution\n\n\u003ca href=\"https://arxiv.org/abs/2511.10647\"\u003e\u003cimg src='https://img.shields.io/badge/arXiv-Depth Anything 3-red' alt='Paper PDF'\u003e\u003c/a\u003e\n\u003ca href='https://depth-anything-3.github.io'\u003e\u003cimg src='https://img.shields.io/badge/Project_Page-Depth Anything 3-green' alt='Project Page'\u003e\u003c/a\u003e\n\u003ca href='https://huggingface.co/spaces/depth-anything/Depth-Anything-3'\u003e\u003cimg src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue'\u003e\u003c/a\u003e\n\u003c!-- \u003ca href='https://huggingface.co/datasets/depth-anything/VGB'\u003e\u003cimg src='https://img.shields.io/badge/Benchmark-VisGeo-yellow' alt='Benchmark'\u003e\u003c/a\u003e --\u003e\n\u003c!-- \u003ca href='https://huggingface.co/datasets/depth-anything/data'\u003e\u003cimg src='https://img.shields.io/badge/Benchmark-xxx-yellow' alt='Data'\u003e\u003c/a\u003e --\u003e\n\n\u003c/div\u003e\n\nThis work presents **Depth Anything 3 (DA3)**, a model that predicts spatially consistent geometry from\narbitrary visual inputs, with or without known camera poses.\nIn pursuit of minimal modeling, DA3 yields two key insights:\n- 💎 A **single plain transformer** (e.g., vanilla DINO encoder) is sufficient as a backbone without architectural specialization,\n- ✨ A singular **depth-ray representation** obviates the need for complex multi-task learning.\n\n🏆 DA3 significantly outperforms\n[DA2](https://github.com/DepthAnything/Depth-Anything-V2) for monocular depth estimation,\nand [VGGT](https://github.com/facebookresearch/vggt) for multi-view depth estimation and pose estimation.\nAll models are trained exclusively on **public academic datasets**.\n\n\u003c!-- \u003cp align=\"center\"\u003e\n  \u003cimg src=\"assets/images/da3_teaser.png\" alt=\"Depth Anything 3\" width=\"100%\"\u003e\n\u003c/p\u003e --\u003e\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"assets/images/demo320-2.gif\" alt=\"Depth Anything 3 - Left\" width=\"70%\"\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"assets/images/da3_radar.png\" alt=\"Depth Anything 3\" width=\"100%\"\u003e\n\u003c/p\u003e\n\n\n## 📰 News\n- **11-12-2025:** 🚀 New models and [**DA3-Streaming**](da3_streaming/README.md) released! Handle ultra-long video sequence inference with less than 12GB GPU memory via sliding-window streaming inference. Special thanks to [Kai Deng](https://github.com/DengKaiCQ) for his contribution to DA3-Streaming!\n- **08-12-2025:** 📊 [Benchmark evaluation pipeline](docs/BENCHMARK.md) released! Evaluate pose estimation \u0026 3D reconstruction on 5 datasets.\n- **30-11-2025:** Add [`use_ray_pose`](#use-ray-pose) and [`ref_view_strategy`](docs/funcs/ref_view_strategy.md) (reference view selection for multi-view inputs).   \n- **25-11-2025:** Add [Awesome DA3 Projects](#-awesome-da3-projects), a community-driven section featuring DA3-based applications.\n- **14-11-2025:** Paper, project page, code and models are all released.\n\n## ✨ Highlights\n\n### 🏆 Model Zoo\nWe release three series of models, each tailored for specific use cases in visual geometry.\n\n- 🌟 **DA3 Main Series** (`DA3-Giant`, `DA3-Large`, `DA3-Base`, `DA3-Small`) These are our flagship foundation models, trained with a unified depth-ray representation. By varying the input configuration, a single model can perform a wide range of tasks:\n  + 🌊 **Monocular Depth Estimation**: Predicts a depth map from a single RGB image.\n  + 🌊 **Multi-View Depth Estimation**: Generates consistent depth maps from multiple images for high-quality fusion.\n  + 🎯 **Pose-Conditioned Depth Estimation**: Achieves superior depth consistency when camera poses are provided as input.\n  + 📷 **Camera Pose Estimation**:  Estimates camera extrinsics and intrinsics from one or more images.\n  + 🟡 **3D Gaussian Estimation**: Directly predicts 3D Gaussians, enabling high-fidelity novel view synthesis.\n\n- 📐 **DA3 Metric Series** (`DA3Metric-Large`) A specialized model fine-tuned for metric depth estimation in monocular settings, ideal for applications requiring real-world scale.\n\n- 🔍 **DA3 Monocular Series** (`DA3Mono-Large`). A dedicated model for high-quality relative monocular depth estimation. Unlike disparity-based models (e.g.,  [Depth Anything 2](https://github.com/DepthAnything/Depth-Anything-V2)), it directly predicts depth, resulting in superior geometric accuracy.\n\n🔗 Leveraging these available models, we developed a **nested series** (`DA3Nested-Giant-Large`). This series combines a any-view giant model with a metric model to reconstruct visual geometry at a real-world metric scale.\n\n### 🛠️ Codebase Features\nOur repository is designed to be a powerful and user-friendly toolkit for both practical application and future research.\n- 🎨 **Interactive Web UI \u0026 Gallery**: Visualize model outputs and compare results with an easy-to-use Gradio-based web interface.\n- ⚡ **Flexible Command-Line Interface (CLI)**: Powerful and scriptable CLI for batch processing and integration into custom workflows.\n- 💾 **Multiple Export Formats**: Save your results in various formats, including `glb`, `npz`, depth images, `ply`, 3DGS videos, etc, to seamlessly connect with other tools.\n- 🔧 **Extensible and Modular Design**: The codebase is structured to facilitate future research and the integration of new models or functionalities.\n\n\n\u003c!-- ### 🎯 Visual Geometry Benchmark\nWe introduce a new benchmark to rigorously evaluate geometry prediction models on three key tasks: pose estimation, 3D reconstruction, and visual rendering (novel view synthesis) quality.\n\n- 🔄 **Broad Model Compatibility**: Our benchmark is designed to be versatile, supporting the evaluation of various models, including both monocular and multi-view depth estimation approaches.\n- 🔬 **Robust Evaluation Pipeline**: We provide a standardized pipeline featuring RANSAC-based pose alignment, TSDF fusion for dense reconstruction, and a principled view selection strategy for novel view synthesis.\n- 📊 **Standardized Metrics**: Performance is measured using established metrics: AUC for pose accuracy, F1-score and Chamfer Distance for reconstruction, and PSNR/SSIM/LPIPS for rendering quality.\n- 🌍 **Diverse and Challenging Datasets**: The benchmark spans a wide range of scenes from datasets like HiRoom, ETH3D, DTU, 7Scenes, ScanNet++, DL3DV, Tanks and Temples, and MegaDepth. --\u003e\n\n\n## 🚀 Quick Start\n\n### 📦 Installation\n\n```bash\npip install xformers torch\\\u003e=2 torchvision\npip install -e . # Basic\npip install --no-build-isolation git+https://github.com/nerfstudio-project/gsplat.git@0b4dddf04cb687367602c01196913cde6a743d70 # for gaussian head\npip install -e \".[app]\" # Gradio, python\u003e=3.10\npip install -e \".[all]\" # ALL\n```\n\nFor detailed model information, please refer to the [Model Cards](#-model-cards) section below.\n\n### 💻 Basic Usage\n\n```python\nimport glob, os, torch\nfrom depth_anything_3.api import DepthAnything3\ndevice = torch.device(\"cuda\")\nmodel = DepthAnything3.from_pretrained(\"depth-anything/DA3NESTED-GIANT-LARGE\")\nmodel = model.to(device=device)\nexample_path = \"assets/examples/SOH\"\nimages = sorted(glob.glob(os.path.join(example_path, \"*.png\")))\nprediction = model.inference(\n    images,\n)\n# prediction.processed_images : [N, H, W, 3] uint8   array\nprint(prediction.processed_images.shape)\n# prediction.depth            : [N, H, W]    float32 array\nprint(prediction.depth.shape)  \n# prediction.conf             : [N, H, W]    float32 array\nprint(prediction.conf.shape)  \n# prediction.extrinsics       : [N, 3, 4]    float32 array # opencv w2c or colmap format\nprint(prediction.extrinsics.shape)\n# prediction.intrinsics       : [N, 3, 3]    float32 array\nprint(prediction.intrinsics.shape)\n```\n\n```bash\n\nexport MODEL_DIR=depth-anything/DA3NESTED-GIANT-LARGE\n# This can be a Hugging Face repository or a local directory\n# If you encounter network issues, consider using the following mirror: export HF_ENDPOINT=https://hf-mirror.com\n# Alternatively, you can download the model directly from Hugging Face\nexport GALLERY_DIR=workspace/gallery\nmkdir -p $GALLERY_DIR\n\n# CLI auto mode with backend reuse\nda3 backend --model-dir ${MODEL_DIR} --gallery-dir ${GALLERY_DIR} # Cache model to gpu\nda3 auto assets/examples/SOH \\\n    --export-format glb \\\n    --export-dir ${GALLERY_DIR}/TEST_BACKEND/SOH \\\n    --use-backend\n\n# CLI video processing with feature visualization\nda3 video assets/examples/robot_unitree.mp4 \\\n    --fps 15 \\\n    --use-backend \\\n    --export-dir ${GALLERY_DIR}/TEST_BACKEND/robo \\\n    --export-format glb-feat_vis \\\n    --feat-vis-fps 15 \\\n    --process-res-method lower_bound_resize \\\n    --export-feat \"11,21,31\"\n\n# CLI auto mode without backend reuse\nda3 auto assets/examples/SOH \\\n    --export-format glb \\\n    --export-dir ${GALLERY_DIR}/TEST_CLI/SOH \\\n    --model-dir ${MODEL_DIR}\n\n```\n\nThe model architecture is defined in [`DepthAnything3Net`](src/depth_anything_3/model/da3.py), and specified with a Yaml config file located at [`src/depth_anything_3/configs`](src/depth_anything_3/configs). The input and output processing are handled by [`DepthAnything3`](src/depth_anything_3/api.py). To customize the model architecture, simply create a new config file (*e.g.*, `path/to/new/config`) as:\n\n```yaml\n__object__:\n  path: depth_anything_3.model.da3\n  name: DepthAnything3Net\n  args: as_params\n\nnet:\n  __object__:\n    path: depth_anything_3.model.dinov2.dinov2\n    name: DinoV2\n    args: as_params\n\n  name: vitb\n  out_layers: [5, 7, 9, 11]\n  alt_start: 4\n  qknorm_start: 4\n  rope_start: 4\n  cat_token: True\n\nhead:\n  __object__:\n    path: depth_anything_3.model.dualdpt\n    name: DualDPT\n    args: as_params\n\n  dim_in: \u0026head_dim_in 1536\n  output_dim: 2\n  features: \u0026head_features 128\n  out_channels: \u0026head_out_channels [96, 192, 384, 768]\n```\n\nThen, the model can be created with the following code snippet.\n```python\nfrom depth_anything_3.cfg import create_object, load_config\n\nModel = create_object(load_config(\"path/to/new/config\"))\n```\n\n\n\n## 📚 Useful Documentation\n\n- 🖥️ [Command Line Interface](docs/CLI.md)\n- 📑 [Python API](docs/API.md)\n- 📊 [Benchmark Evaluation](docs/BENCHMARK.md)\n\n## 🗂️ Model Cards\n\nGenerally, you should observe that DA3-LARGE achieves comparable results to VGGT.\n\nThe Nested series uses an Any-view model to estimate pose and depth, and a monocular metric depth estimator for scaling. \n\n⚠️ Models with the `-1.1` suffix are retrained after fixing a training bug; prefer these refreshed checkpoints. The original `DA3NESTED-GIANT-LARGE`, `DA3-GIANT`, and `DA3-LARGE` remain available but are deprecated. You could expect much better performance for street scenes with the `-1.1` models.\n\n| 🗃️ Model Name                  | 📏 Params | 📊 Rel. Depth | 📷 Pose Est. | 🧭 Pose Cond. | 🎨 GS | 📐 Met. Depth | ☁️ Sky Seg | 📄 License     |\n|-------------------------------|-----------|---------------|--------------|---------------|-------|---------------|-----------|----------------|\n| **Nested** | | | | | | | | |\n| [DA3NESTED-GIANT-LARGE-1.1](https://huggingface.co/depth-anything/DA3NESTED-GIANT-LARGE-1.1)  | 1.40B     | ✅             | ✅            | ✅             | ✅     | ✅             | ✅         | CC BY-NC 4.0   |\n| [DA3NESTED-GIANT-LARGE](https://huggingface.co/depth-anything/DA3NESTED-GIANT-LARGE)  | 1.40B     | ✅             | ✅            | ✅             | ✅     | ✅             | ✅         | CC BY-NC 4.0   |\n| **Any-view Model** | | | | | | | | |\n| [DA3-GIANT-1.1](https://huggingface.co/depth-anything/DA3-GIANT-1.1)                     | 1.15B     | ✅             | ✅            | ✅             | ✅     |               |           | CC BY-NC 4.0   |\n| [DA3-GIANT](https://huggingface.co/depth-anything/DA3-GIANT)                     | 1.15B     | ✅             | ✅            | ✅             | ✅     |               |           | CC BY-NC 4.0   |\n| [DA3-LARGE-1.1](https://huggingface.co/depth-anything/DA3-LARGE-1.1)                     | 0.35B     | ✅             | ✅            | ✅             |       |               |           | CC BY-NC 4.0     |\n| [DA3-LARGE](https://huggingface.co/depth-anything/DA3-LARGE)                     | 0.35B     | ✅             | ✅            | ✅             |       |               |           | CC BY-NC 4.0     |\n| [DA3-BASE](https://huggingface.co/depth-anything/DA3-BASE)                     | 0.12B     | ✅             | ✅            | ✅             |       |               |           | Apache 2.0     |\n| [DA3-SMALL](https://huggingface.co/depth-anything/DA3-SMALL)                     | 0.08B     | ✅             | ✅            | ✅             |       |               |           | Apache 2.0     |\n|                               |           |               |              |               |               |       |           |                |\n| **Monocular Metric Depth** | | | | | | | | |\n| [DA3METRIC-LARGE](https://huggingface.co/depth-anything/DA3METRIC-LARGE)              | 0.35B     | ✅             |              |               |       | ✅             | ✅         | Apache 2.0     |\n|                               |           |               |              |               |               |       |           |                |\n| **Monocular Depth** | | | | | | | | |\n| [DA3MONO-LARGE](https://huggingface.co/depth-anything/DA3MONO-LARGE)                | 0.35B     | ✅             |              |               |               |       | ✅         | Apache 2.0     |\n\n\n## ❓ FAQ\n\n- **Monocular Metric Depth**: To obtain metric depth in meters from `DA3METRIC-LARGE`, use `metric_depth = focal * net_output / 300.`, where `focal` is the focal length in pixels (typically the average of fx and fy from the camera intrinsic matrix K). Note that the output from `DA3NESTED-GIANT-LARGE` is already in meters.\n\n- \u003ca id=\"use-ray-pose\"\u003e\u003c/a\u003e**Ray Head (`use_ray_pose`)**:  Our API and CLI support `use_ray_pose` arg, which means that the model will derive camera pose from ray head, which is generally slightly slower, but more accurate. Note that the default is `False` for faster inference speed. \n  \u003cdetails\u003e\n  \u003csummary\u003eAUC3 Results for DA3NESTED-GIANT-LARGE\u003c/summary\u003e\n  \n  | Model | HiRoom | ETH3D | DTU | 7Scenes | ScanNet++ | \n  |-------|------|-------|-----|---------|-----------|\n  | `ray_head` | 84.4 | 52.6 | 93.9 | 29.5 | 89.4 |\n  | `cam_head` | 80.3 | 48.4 | 94.1 | 28.5 | 85.0 |\n\n  \u003c/details\u003e\n\n\n\n\n- **Older GPUs without XFormers support**: See [Issue #11](https://github.com/ByteDance-Seed/Depth-Anything-3/issues/11). Thanks to [@S-Mahoney](https://github.com/S-Mahoney) for the solution!\n\n\n## 🏢 Awesome DA3 Projects\n\nA community-curated list of Depth Anything 3 integrations across 3D tools, creative pipelines, robotics, and web/VR viewers, including but not limited to these. You are welcome to submit your DA3-based project via PR, and we will review and feature it if applicable.\n\n- [DA3-blender](https://github.com/xy-gao/DA3-blender): Blender addon for DA3-based 3D reconstruction from a set of images. \n\n- [ComfyUI-DepthAnythingV3](https://github.com/PozzettiAndrea/ComfyUI-DepthAnythingV3): ComfyUI nodes for Depth Anything 3, supporting single/multi-view and video-consistent depth with optional point‑cloud export.\n\n- [DA3-ROS2-Wrapper](https://github.com/GerdsenAI/GerdsenAI-Depth-Anything-3-ROS2-Wrapper): Real-time DA3 depth in ROS2 with multi-camera support. \n\n- [DA3-ROS2-CPP-TensorRT](https://github.com/ika-rwth-aachen/ros2-depth-anything-v3-trt): DA3 ROS2 C++ TensorRT Inference Node: a ROS2 node for DA3 depth estimation using TensorRT for real-time inference.\n\n- [VideoDepthViewer3D](https://github.com/amariichi/VideoDepthViewer3D): Streaming videos with DA3 metric depth to a Three.js/WebXR 3D viewer for VR/stereo playback.\n\n\n## 🧑‍💻 Official Codebase Core Contributors and Maintainers\n\n\u003ctable\u003e\n  \u003ctr\u003e\n    \u003ctd align=\"center\"\u003e\n      \u003ca href=\"https://bingykang.github.io/\"\u003e\n        \u003cimg src=\"https://images.weserv.nl/?url=https://bingykang.github.io/images/bykang_homepage.jpeg?h=100\u0026w=100\u0026fit=cover\u0026mask=circle\u0026maxage=7d\" width=\"100px;\" alt=\"\"/\u003e\n      \u003c/a\u003e\n        \u003cbr /\u003e\n        \u003csub\u003e\u003cb\u003eBingyi Kang\u003c/b\u003e\u003c/sub\u003e\n    \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\n      \u003ca href=\"https://haotongl.github.io/\"\u003e\n        \u003cimg src=\"https://images.weserv.nl/?url=https://haotongl.github.io/assets/img/prof_pic.jpg?h=100\u0026w=100\u0026fit=cover\u0026mask=circle\u0026maxage=7d\" width=\"100px;\" alt=\"\"/\u003e\n      \u003c/a\u003e\n        \u003cbr /\u003e\n        \u003csub\u003eHaotong Lin\u003c/sub\u003e\n    \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\n      \u003ca href=\"https://github.com/SiliChen321\"\u003e\n        \u003cimg src=\"https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/195901058?v=4\u0026h=100\u0026w=100\u0026fit=cover\u0026mask=circle\u0026maxage=7d\" width=\"100px;\" alt=\"\"/\u003e\n      \u003c/a\u003e\n        \u003cbr /\u003e\n        \u003csub\u003eSili Chen\u003c/sub\u003e\n    \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\n      \u003ca href=\"https://liewjunhao.github.io/\"\u003e\n        \u003cimg src=\"https://images.weserv.nl/?url=https://liewjunhao.github.io/images/liewjunhao.png?h=100\u0026w=100\u0026fit=cover\u0026mask=circle\u0026maxage=7d\" width=\"100px;\" alt=\"\"/\u003e\n       \u003c/a\u003e\n        \u003cbr /\u003e\n        \u003csub\u003eJun Hao Liew\u003c/sub\u003e\n    \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\n      \u003ca href=\"https://donydchen.github.io/\"\u003e\n        \u003cimg src=\"https://images.weserv.nl/?url=https://donydchen.github.io/assets/img/profile.jpg?h=100\u0026w=100\u0026fit=cover\u0026mask=circle\u0026maxage=7d\" width=\"100px;\" alt=\"\"/\u003e\n      \u003c/a\u003e\n        \u003cbr /\u003e\n        \u003csub\u003eDonny Y. Chen\u003c/sub\u003e\n    \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\n      \u003ca href=\"https://github.com/DengKaiCQ\"\u003e\n        \u003cimg src=\"https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/59907452?v=4\u0026h=100\u0026w=100\u0026fit=cover\u0026mask=circle\u0026maxage=7d\" width=\"100px;\" alt=\"\"/\u003e\n      \u003c/a\u003e\n        \u003cbr /\u003e\n        \u003csub\u003eKai Deng\u003c/sub\u003e\n    \u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\n## 📝 Citations\nIf you find Depth Anything 3 useful in your research or projects, please cite our work:\n\n```\n@article{depthanything3,\n  title={Depth Anything 3: Recovering the visual space from any views},\n  author={Haotong Lin and Sili Chen and Jun Hao Liew and Donny Y. Chen and Zhenyu Li and Guang Shi and Jiashi Feng and Bingyi Kang},\n  journal={arXiv preprint arXiv:2511.10647},\n  year={2025}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FByteDance-Seed%2FDepth-Anything-3","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FByteDance-Seed%2FDepth-Anything-3","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FByteDance-Seed%2FDepth-Anything-3/lists"}