SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding

Baoxiong Jia^✶, Yixin Chen^✶, Huangyue Yu, Yan Wang, Xuesong Niu, Tengyu Liu, Qing Li, Siyuan Huang

 

 

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 rel2	Ref-MultiObject relm	Ref-Star star	Ref-Chain (Optional) chain
ScanNet	✅	✅	ScanRefer 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) 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

@inproceedings{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},
  booktitle={European Conference on Computer Vision (ECCV)},
  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.