{"id":22067942,"url":"https://xuxw98.github.io/ESAM/","last_synced_at":"2025-07-24T05:31:21.946Z","repository":{"id":254097195,"uuid":"727103787","full_name":"xuxw98/ESAM","owner":"xuxw98","description":"EmbodiedSAM: Online Segment Any 3D Thing in Real Time","archived":false,"fork":false,"pushed_at":"2024-11-01T08:19:34.000Z","size":62702,"stargazers_count":223,"open_issues_count":0,"forks_count":14,"subscribers_count":4,"default_branch":"main","last_synced_at":"2024-11-19T09:23:48.662Z","etag":null,"topics":["3d-instance-segmentation","3d-scene-understanding","embodied-vision","real-time","segment-anything","semi-supervised","streaming-video"],"latest_commit_sha":null,"homepage":"https://xuxw98.github.io/ESAM/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/xuxw98.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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}},"created_at":"2023-12-04T07:34:11.000Z","updated_at":"2024-11-19T01:53:46.000Z","dependencies_parsed_at":"2024-10-26T14:41:14.830Z","dependency_job_id":null,"html_url":"https://github.com/xuxw98/ESAM","commit_stats":null,"previous_names":["xuxw98/esam"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xuxw98%2FESAM","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xuxw98%2FESAM/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xuxw98%2FESAM/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xuxw98%2FESAM/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/xuxw98","download_url":"https://codeload.github.com/xuxw98/ESAM/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":227421234,"owners_count":17775005,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["3d-instance-segmentation","3d-scene-understanding","embodied-vision","real-time","segment-anything","semi-supervised","streaming-video"],"created_at":"2024-11-30T20:03:38.414Z","updated_at":"2025-07-24T05:31:21.934Z","avatar_url":"https://github.com/xuxw98.png","language":"Python","funding_links":[],"categories":["Paper List"],"sub_categories":["Follow-up Papers"],"readme":"# EmbodiedSAM: Online Segment Any 3D Thing in Real Time\r\n### [Paper](https://arxiv.org/abs/2408.11811) | [Project Page](https://xuxw98.github.io/ESAM/) | [Video](https://cloud.tsinghua.edu.cn/f/000910bee9d5476db250/?dl=1) | [中文解读](https://zhuanlan.zhihu.com/p/23105869992)\r\n\r\n\u003e EmbodiedSAM: Online Segment Any 3D Thing in Real Time  \r\n\u003e [Xiuwei Xu](https://xuxw98.github.io/), Huangxing Chen, [Linqing Zhao](https://scholar.google.com/citations?user=ypxt5UEAAAAJ\u0026hl=zh-CN\u0026oi=ao), [Ziwei Wang](https://ziweiwangthu.github.io/), [Jie Zhou](https://scholar.google.com/citations?user=6a79aPwAAAAJ\u0026hl=en\u0026authuser=1), [Jiwen Lu](http://ivg.au.tsinghua.edu.cn/Jiwen_Lu/)\r\n\r\n\r\nIn this work, we presented ESAM, an efficient framework that leverages vision foundation models for \u003cb\u003eonline\u003c/b\u003e, \u003cb\u003ereal-time\u003c/b\u003e, \u003cb\u003efine-grained\u003c/b\u003e, \u003cb\u003egeneralized\u003c/b\u003e and \u003cb\u003eopen-vocabulary\u003c/b\u003e 3D instance segmentation. \r\n\r\n\r\n## News\r\n- [2025/4/03]: Custom dataset is supported! Users can run EmbodiedSAM on their own data following [here](./docs/demo.md).\r\n- [2025/2/11]: EmbodiedSAM is selected as an \u003cb\u003eoral presentation\u003c/b\u003e in ICLR 2025!\r\n- [2025/1/23]: EmbodiedSAM is accepted to ICLR 2025 with a top 2% rating!\r\n- [2024/8/22]: Code and demo released.\r\n\r\n\r\n## Demo\r\n### Real-world:\r\n![demo](./assets/real-world.gif)\r\n\r\n### Bedroom:\r\n\u003cimg src=\"./assets/demo2.gif\" width=\"450\" /\u003e\r\n\r\n### Office:\r\n\u003cimg src=\"./assets/demo1.gif\" width=\"450\" /\u003e\r\n\r\nDemos are a little bit large; please wait a moment to load them. Welcome to the home page for more complete demos and detailed introductions.\r\n\r\n\r\n## Method \r\n\r\nMethod Pipeline:\r\n![overview](./assets/pipeline.png)\r\n\r\n## Getting Started\r\nFor environment setup and dataset preparation, please follow:\r\n* [Installation](./docs/installation.md)\r\n* [Dataset Preparation](./docs/dataset_preparation.md)\r\n\r\nFor training and evaluation, please follow:\r\n* [Train and Evaluation](./docs/run.md)\r\n  \r\nFor visualization on the provided datasets or **your own data**, please follow:\r\n* [Visualization Demo](./docs/demo.md)\r\n\r\n\r\n## Main Results\r\nWe provide the checkpoints for quick reproduction of the results reported in the paper. In addition to Tsinghua Cloud, we also upload the checkpoints and processed data to HuggingFace. Click [here](https://huggingface.co/XXXCARREY/EmbodiedSAM/tree/main) for more details.\r\n\r\n**Class-agnostic 3D instance segmentation results on ScanNet200 dataset:**\r\n\r\n|  Method  |   Type  |     VFM     |  AP  | AP@50 | AP@25 | Speed(ms) | Downloads |\r\n|:--------:|:-------:|:-----------:|:----:|:-----:|:-----:|:---------:|:---------:|\r\n| [SAMPro3D](https://github.com/GAP-LAB-CUHK-SZ/SAMPro3D) | Offline |     [SAM](https://github.com/facebookresearch/segment-anything)     | 18.0 |  32.8 |  56.1 |     --    |     --    |\r\n|   [SAI3D](https://github.com/yd-yin/SAI3D)  | Offline | [SemanticSAM](https://github.com/UX-Decoder/Semantic-SAM) | 30.8 |  50.5 |  70.6 |     --    |     --    |\r\n|   [SAM3D](https://github.com/Pointcept/SegmentAnything3D)  |  Online |     SAM     | 20.6 |  35.7 |  55.5 | 1369+1518 |     --    |\r\n|   ESAM   |  Online |     SAM     | 42.2 |  63.7 |  79.6 |  1369+**80**  |   [model](https://cloud.tsinghua.edu.cn/f/426d6eb693ff4b1fa04b/?dl=1)   |\r\n|  ESAM-E  |  Online |   [FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM)   | **43.4** |  **65.4** |  **80.9** |   **20**+**80**   |   [model](https://cloud.tsinghua.edu.cn/f/7578d7e3d6764f6a93ee/?dl=1)   |\r\n\r\n**Dataset transfer results from ScanNet200 to SceneNN and 3RScan:**\r\n\u003ctable class=\"tg\"\u003e\u003cthead\u003e\r\n  \u003ctr\u003e\r\n    \u003cth class=\"tg-b2st\" rowspan=\"2\"\u003eMethod\u003c/th\u003e\r\n    \u003cth class=\"tg-b2st\" rowspan=\"2\"\u003eType \u003c/th\u003e\r\n    \u003cth class=\"tg-b2st\" colspan=\"3\"\u003eScanNet200--\u0026gt;SceneNN\u003c/th\u003e\r\n    \u003cth class=\"tg-b2st\" colspan=\"3\"\u003eScanNet200--\u0026gt;3RScan\u003c/th\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003cth class=\"tg-wa1i\"\u003eAP\u003c/th\u003e\r\n    \u003cth class=\"tg-wa1i\"\u003eAP@50\u003c/th\u003e\r\n    \u003cth class=\"tg-wa1i\"\u003eAP@25\u003c/th\u003e\r\n    \u003cth class=\"tg-wa1i\"\u003eAP\u003c/th\u003e\r\n    \u003cth class=\"tg-wa1i\"\u003eAP@50\u003c/th\u003e\r\n    \u003cth class=\"tg-wa1i\"\u003eAP@25\u003c/th\u003e\r\n  \u003c/tr\u003e\u003c/thead\u003e\r\n\u003ctbody\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003eSAMPro3D\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003eOffline\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e12.6\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e25.8\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e53.2\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e3.9\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e8.0\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e21.0\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003eSAI3D\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003eOffline\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e18.6\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e34.7\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e65.7\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e5.4\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e11.8\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e27.4\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003eSAM3D\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003eOnline\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e15.1\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e30.0\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e51.8\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e6.2\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e13.0\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e33.9\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003eESAM\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003eOnline\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e\u003cb\u003e28.8\u003c/b\u003e\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e\u003cb\u003e52.2\u003c/b\u003e\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e69.3\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e\u003cb\u003e14.1\u003c/b\u003e\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e\u003cb\u003e31.2\u003c/b\u003e\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e\u003cb\u003e59.6\u003c/b\u003e\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003eESAM-E\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003eOnline\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e28.6\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e50.4\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e\u003cb\u003e71.0\u003c/b\u003e\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e13.9\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e29.4\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e58.8\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n\u003c/tbody\u003e\u003c/table\u003e\r\n\r\n**3D instance segmentation results on ScanNet dataset:**\r\n\u003ctable class=\"tg\"\u003e\u003cthead\u003e\r\n  \u003ctr\u003e\r\n    \u003cth class=\"tg-gabo\" rowspan=\"2\"\u003eMethod\u003c/th\u003e\r\n    \u003cth class=\"tg-gabo\" rowspan=\"2\"\u003eType\u003c/th\u003e\r\n    \u003cth class=\"tg-gabo\" colspan=\"3\"\u003eScanNet\u003c/th\u003e\r\n    \u003cth class=\"tg-gabo\" colspan=\"3\"\u003eSceneNN\u003c/th\u003e\r\n    \u003cth class=\"tg-gabo\" rowspan=\"2\"\u003eFPS\u003c/th\u003e\r\n    \u003cth class=\"tg-gabo\" rowspan=\"2\"\u003eDownload\u003c/th\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003cth class=\"tg-uzvj\"\u003eAP\u003c/th\u003e\r\n    \u003cth class=\"tg-uzvj\"\u003eAP@50\u003c/th\u003e\r\n    \u003cth class=\"tg-uzvj\"\u003eAP@25\u003c/th\u003e\r\n    \u003cth class=\"tg-uzvj\"\u003eAP\u003c/th\u003e\r\n    \u003cth class=\"tg-uzvj\"\u003eAP@50\u003c/th\u003e\r\n    \u003cth class=\"tg-uzvj\"\u003eAP@25\u003c/th\u003e\r\n  \u003c/tr\u003e\u003c/thead\u003e\r\n\u003ctbody\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e\u003ca href=https://github.com/SamsungLabs/td3d\u003eTD3D\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003eoffline\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e46.2\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e71.1\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e81.3\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e--\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e--\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e--\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e--\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e--\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e\u003ca href=https://github.com/oneformer3d/oneformer3d\u003eOneformer3D\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003eoffline\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e59.3\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e78.8\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e86.7\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e--\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e--\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e--\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e--\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e--\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e\u003ca href=https://github.com/THU-luvision/INS-Conv\u003eINS-Conv\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003eonline\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e--\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e57.4\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e--\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e--\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e--\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e--\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e--\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e--\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e\u003ca href=https://github.com/xuxw98/Online3D\u003eTD3D-MA\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003eonline\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e39.0\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e60.5\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e71.3\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e26.0\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e42.8\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e59.2\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e3.5\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e--\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003eESAM-E\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003eonline\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e41.6\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e60.1\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e75.6\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e27.5\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e48.7\u003c/td\u003e\r\n    \u003ctd class=\"tg-uzvj\"\u003e\u003cb\u003e64.6\u003c/b\u003e\u003c/td\u003e\r\n    \u003ctd class=\"tg-uzvj\"\u003e\u003cb\u003e10\u003c/b\u003e\u003c/td\u003e\r\n    \u003ctd class=\"tg-9wq8\"\u003e\u003ca href=https://cloud.tsinghua.edu.cn/f/1eeff1152a5f4d4989da/?dl=1\u003emodel\u003c/a\u003e\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003eESAM-E+FF\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003eonline\u003c/td\u003e\r\n    \u003ctd class=\"tg-wa1i\"\u003e\u003cb\u003e42.6\u003c/b\u003e\u003c/td\u003e\r\n    \u003ctd class=\"tg-wa1i\"\u003e\u003cb\u003e61.9\u003c/b\u003e\u003c/td\u003e\r\n    \u003ctd class=\"tg-wa1i\"\u003e\u003cb\u003e77.1\u003c/b\u003e\u003c/td\u003e\r\n    \u003ctd class=\"tg-wa1i\"\u003e\u003cb\u003e33.3\u003c/b\u003e\u003c/td\u003e\r\n    \u003ctd class=\"tg-wa1i\"\u003e\u003cb\u003e53.6\u003c/b\u003e\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e62.5\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e9.8\u003c/td\u003e\r\n    \u003ctd class=\"tg-nrix\"\u003e\u003ca href=https://cloud.tsinghua.edu.cn/f/4c2dd1559e854f48be76/?dl=1\u003emodel\u003c/a\u003e\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n\u003c/tbody\u003e\u003c/table\u003e\r\n\r\n**Open-Vocabulary 3D instance segmentation results on ScanNet200 dataset:**\r\n| Method |  AP  | AP@50 | AP@25 |\r\n|:------:|:----:|:-----:|:-----:|\r\n|  SAI3D |  9.6 |  14.7 |  19.0 |\r\n|  ESAM  | **13.7** |  **19.2** |  **23.9** |\r\n\r\n\r\n## TODO List\r\n- [x] Release code and checkpoints.\r\n- [x] Release the demo code to directly run ESAM on streaming RGB-D video.\r\n\r\n## Contributors\r\nBoth students below contributed equally and the order is determined by random draw.\r\n- [Xiuwei Xu](https://xuxw98.github.io/)\r\n- Huangxing Chen\r\n\r\nBoth advised by [Jiwen Lu](https://ivg.au.tsinghua.edu.cn/Jiwen_Lu/).\r\n\r\n## Acknowledgement\r\nWe thank a lot for the flexible codebase of [Oneformer3D](https://github.com/oneformer3d/oneformer3d) and [Online3D](https://github.com/xuxw98/Online3D), as well as the valuable datasets provided by [ScanNet](https://github.com/ScanNet/ScanNet), [SceneNN](https://github.com/hkust-vgd/scenenn) and [3RScan](https://github.com/WaldJohannaU/3RScan).\r\n\r\n\r\n## Citation\r\n```\r\n@article{xu2024esam, \r\n      title={EmbodiedSAM: Online Segment Any 3D Thing in Real Time}, \r\n      author={Xiuwei Xu and Huangxing Chen and Linqing Zhao and Ziwei Wang and Jie Zhou and Jiwen Lu},\r\n      journal={arXiv preprint arXiv:2408.11811},\r\n      year={2024}\r\n}\r\n```\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/xuxw98.github.io%2FESAM%2F","html_url":"https://awesome.ecosyste.ms/projects/xuxw98.github.io%2FESAM%2F","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/xuxw98.github.io%2FESAM%2F/lists"}