Update README.md
Browse files
README.md
CHANGED
@@ -1,3 +1,30 @@
|
|
1 |
-
---
|
2 |
-
license: mit
|
3 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
license: mit
|
3 |
+
pipeline_tag: visual-question-answering
|
4 |
+
---
|
5 |
+
# 3DGraphLLM
|
6 |
+
|
7 |
+
3DGraphLLM is a model that uses a 3D scene graph and an LLM to perform 3D vision-language tasks.
|
8 |
+
|
9 |
+
<p align="center">
|
10 |
+
<img src="ga.png" width="80%">
|
11 |
+
</p>
|
12 |
+
|
13 |
+
|
14 |
+
## Model Details
|
15 |
+
|
16 |
+
We provide our best checkpoint that uses [Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as an LLM, [Mask3D](https://github.com/JonasSchult/Mask3D) 3D instance segmentation to get scene graph nodes, [VL-SAT](https://github.com/wz7in/CVPR2023-VLSAT) to encode semantic relations [Uni3D](https://github.com/baaivision/Uni3D) as 3D object encoder, and [DINOv2](https://github.com/facebookresearch/dinov2) as 2D object encoder.
|
17 |
+
|
18 |
+
## Citation
|
19 |
+
If you find 3DGraphLLM helpful, please consider citing our work as:
|
20 |
+
```
|
21 |
+
@misc{zemskova20243dgraphllm,
|
22 |
+
title={3DGraphLLM: Combining Semantic Graphs and Large Language Models for 3D Scene Understanding},
|
23 |
+
author={Tatiana Zemskova and Dmitry Yudin},
|
24 |
+
year={2024},
|
25 |
+
eprint={2412.18450},
|
26 |
+
archivePrefix={arXiv},
|
27 |
+
primaryClass={cs.CV},
|
28 |
+
url={https://arxiv.org/abs/2412.18450},
|
29 |
+
}
|
30 |
+
```
|