EmbodiedSAM: Online Segment Any 3D Thing in Real Time
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EmbodiedSAM: Online Segment Any 3D Thing in Real Time
Xiuwei Xu, Huangxing Chen, Linqing Zhao, Ziwei Wang, Jie Zhou, Jiwen Lu
In this work, we presented ESAM, an efficient framework that leverages vision foundation models for online, real-time, fine-grained, generalized and open-vocabulary 3D instance segmentation.
News
- [2025/4/03]: Custom dataset is supported! Users can run EmbodiedSAM on their own data following here.
- [2025/2/11]: EmbodiedSAM is selected as an oral presentation in ICLR 2025!
- [2025/1/23]: EmbodiedSAM is accepted to ICLR 2025 with a top 2% rating!
- [2024/8/22]: Code and demo released.
Demo
Real-world:
Bedroom:
Office:
Demos are a little bit large; please wait a moment to load them. Welcome to the home page for more complete demos and detailed introductions.
Method
Method Pipeline:
Getting Started
For environment setup and dataset preparation, please follow:
For training and evaluation, please follow:
For visualization on the provided datasets or your own data, please follow:
Main Results
We 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 for more details.
Class-agnostic 3D instance segmentation results on ScanNet200 dataset:
| Method | Type | VFM | AP | AP@50 | AP@25 | Speed(ms) | Downloads |
|---|---|---|---|---|---|---|---|
| SAMPro3D | Offline | SAM | 18.0 | 32.8 | 56.1 | -- | -- |
| SAI3D | Offline | SemanticSAM | 30.8 | 50.5 | 70.6 | -- | -- |
| SAM3D | Online | SAM | 20.6 | 35.7 | 55.5 | 1369+1518 | -- |
| ESAM | Online | SAM | 42.2 | 63.7 | 79.6 | 1369+80 | model |
| ESAM-E | Online | FastSAM | 43.4 | 65.4 | 80.9 | 20+80 | model |
Dataset transfer results from ScanNet200 to SceneNN and 3RScan:
Method
Type
ScanNet200-->SceneNN
ScanNet200-->3RScan
AP
AP@50
AP@25
AP
AP@50
AP@25
SAMPro3D
Offline
12.6
25.8
53.2
3.9
8.0
21.0
SAI3D
Offline
18.6
34.7
65.7
5.4
11.8
27.4
SAM3D
Online
15.1
30.0
51.8
6.2
13.0
33.9
ESAM
Online
28.8
52.2
69.3
14.1
31.2
59.6
ESAM-E
Online
28.6
50.4
71.0
13.9
29.4
58.8
3D instance segmentation results on ScanNet dataset:
Method
Type
ScanNet
SceneNN
FPS
Download
AP
AP@50
AP@25
AP
AP@50
AP@25
offline
46.2
71.1
81.3
--
--
--
--
--
offline
59.3
78.8
86.7
--
--
--
--
--
online
--
57.4
--
--
--
--
--
--
online
39.0
60.5
71.3
26.0
42.8
59.2
3.5
--
ESAM-E
online
41.6
60.1
75.6
27.5
48.7
64.6
10
ESAM-E+FF
online
42.6
61.9
77.1
33.3
53.6
62.5
9.8
Open-Vocabulary 3D instance segmentation results on ScanNet200 dataset:
| Method | AP | AP@50 | AP@25 |
|---|---|---|---|
| SAI3D | 9.6 | 14.7 | 19.0 |
| ESAM | 13.7 | 19.2 | 23.9 |
TODO List
- [x] Release code and checkpoints.
- [x] Release the demo code to directly run ESAM on streaming RGB-D video.
Contributors
Both students below contributed equally and the order is determined by random draw.
- Xiuwei Xu
- Huangxing Chen
Both advised by Jiwen Lu.
Acknowledgement
We thank a lot for the flexible codebase of Oneformer3D and Online3D, as well as the valuable datasets provided by ScanNet, SceneNN and 3RScan.
Citation
@article{xu2024esam,
title={EmbodiedSAM: Online Segment Any 3D Thing in Real Time},
author={Xiuwei Xu and Huangxing Chen and Linqing Zhao and Ziwei Wang and Jie Zhou and Jiwen Lu},
journal={arXiv preprint arXiv:2408.11811},
year={2024}
}