Awesome
<h2 align="center"> <span><img src="assets/logo025.png" width="4%" style="transform: translate(0,9px)"></span> <b>SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding</b> </h2> <div align="center" margin-bottom="6em"> <a target="_blank" href="https://buzz-beater.github.io/">Baoxiong Jia<sup>✶</sup></a>, <a target="_blank" href="https://yixchen.github.io/">Yixin Chen<sup>✶</sup></a>, <a target="_blank" href="https://scholar.google.com/citations?user=fKRgnIMAAAAJ/">Huangyue Yu</a>, <a target="_blank" href="https://github.com/jetpackfirstme">Yan Wang</a>, <a target="_blank" href="https://nxsedson.github.io/">Xuesong Niu</a>, <a target="_blank" href="https://tengyu.ai/">Tengyu Liu</a>, <a target="_blank" href="https://liqing-ustc.github.io/">Qing Li</a>, <a target="_blank" href="https://siyuanhuang.com/">Siyuan Huang</a> </div> <div align="center"> <a href="https://arxiv.org/abs/2401.09340" target="_blank"> <img src="https://img.shields.io/badge/Paper-arXiv-deepgreen" alt="Paper arXiv"></a> <a href="https://scene-verse.github.io" target="_blank"> <img src="https://img.shields.io/badge/Project-Page-9cf" alt="Project Page"></a> <a href="https://youtu.be/UnujS0EVxKU" target="_blank"> <img src="https://img.shields.io/badge/Video-YouTube-9966ff" alt="Video"></a> <a href="https://scene-verse.github.io" target="_blank"> <img src="https://img.shields.io/badge/Data-SceneVerse-blue" alt="Data"></a> <a href="https://scene-verse.github.io" target="_blank"> <img src="https://img.shields.io/badge/Model-GPS-darkorange" alt="Model"></a> </div> <div align="left"> <img src="assets/overview.png" width="99%" alt="SceneVerse Teaser"> </div>We propose SceneVerse, the first million-scale 3D vision-language dataset with 68K 3D indoor scenes and 2.5M vision-language pairs. We demonstrate the scaling effect by (i) achieving state-of-the-art on all existing 3D visual grounding benchmarks and (ii) showcasing zero-shot transfer capabilities with our GPS (Grounded Pre-training for Scenes) model.
News
- [2024-10] Pre-trained checkpoints are now available, find detailed instructions in TRAIN.md!
- [2024-09] The scripts for scene graph generation are released.
- [2024-07] Training & Inference code as well as preprocessing code is released and checkpoints & logs are on the way!
- [2024-07] Preprocessing codes for scenes used in SceneVerse are released.
- [2024-07] SceneVerse is accepted by ECCV 2024! Training and inference codes/checkpoints will come shortly, stay tuned!
- [2024-03] We release the data used in SceneVerse. Fill out the form for the download link!
- [2024-01] We release SceneVerse on ArXiv. Checkout our paper and website.
Data
See DATA.md for detailed instructions on data download, processing, visualization. The data inventory is listed below:
Dataset | Object Caption | Scene Caption | Ref-Annotation | Ref-Pairwise<br>rel2 | Ref-MultiObject<br>relm | Ref-Star<br>star | Ref-Chain (Optional)<br>chain |
---|---|---|---|---|---|---|---|
ScanNet | ✅ | ✅ | ScanRefer<br>Nr3D | ✅ | ✅ | ✅ | ✅ |
MultiScan | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
ARKitScenes | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
HM3D | template | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
3RScan | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ |
Structured3D | template | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ |
ProcTHOR | template | ❌ | ❌ | template | template | template | ❌ |
Training and Inference
See TRAIN.md for the inventory of available checkpoints and detailed instructions on training and testing with pre-trained checkpoints. The checkpoint inventory is listed below:
Setting | Description | Corresponding Experiment | Checkpoint based on experiment setting |
---|---|---|---|
pre-trained | GPS model pre-trained on SceneVerse | 3D-VL grounding (Tab.2) | Model |
scratch | GPS model trained on datasets from scratch | 3D-VL grounding (Tab.2)<br/>SceneVerse-val (Tab. 3) | ScanRefer, Sr3D, Nr3D, SceneVerse-val |
fine-tuned | GPS model fine-tuned on datasets with grounding heads | 3D-VL grounding (Tab.2) | ScanRefer, Sr3D, Nr3D |
zero-shot | GPS model trained on SceneVerse without data from ScanNet and MultiScan | Zero-shot Transfer (Tab.3) | Model |
zero-shot text | GPS | Zero-shot Transfer (Tab.3) | ScanNet, SceneVerse-val |
text-ablation | Ablations on the type of language used during pre-training | Ablation on Text (Tab.7) | Template only, Template+LLM |
scene-ablation | Ablations on the use of synthetic scenes during pre-training | Ablation on Scene (Tab.8) | Real only, S3D only, ProcTHOR only |
model-ablation | Ablations on the use of losses during pre-training | Ablation on Model Design (Tab.9) | Refer only, Refer+Obj-lvl, w/o Scene-lvl |
BibTex
@article{jia2024sceneverse,
title={SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding},
author={Jia, Baoxiong and Chen, Yixin and Yu, Huangyue and Wang, Yan and Niu, Xuesong and Liu, Tengyu and Li, Qing and Huang, Siyuan},
journal={arXiv preprint arXiv:2401.09340},
year={2024}
}
Acknowledgements
We thank the authors from ScanRefer, ScanNet, 3RScan, ReferIt3D, Structured3D, HM3D, ProcTHOR, ARKitScenes, MultiScan for open-sourcing their awesome datasets. We also heavily adapted codes from ScanQA, SQA3D, and 3D-VisTA for training and inference.