3DVG-Transformer

This repository is for the ICCV 2021 paper "3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds"

Our method "3DVG-Transformer+" is the 1st method on the ScanRefer benchmark (2021/3 - 2021/11) and is the winner of the CVPR2021 1st Workshop on Language for 3D Scenes

🌟 3DVG-Transformer+ achieves comparable results with papers published in [CVPR2022]. 🌟

Introduction

Visual grounding on 3D point clouds is an emerging vision and language task that benefits various applications in understanding the 3D visual world. By formulating this task as a grounding-by-detection problem, lots of recent works focus on how to exploit more powerful detectors and comprehensive language features, but (1) how to model complex relations for generating context-aware object proposals and (2) how to leverage proposal relations to distinguish the true target object from similar proposals are not fully studied yet. Inspired by the well-known transformer architecture, we propose a relation-aware visual grounding method on 3D point clouds, named as 3DVG-Transformer, to fully utilize the contextual clues for relation-enhanced proposal generation and cross-modal proposal disambiguation, relation-aware proposal generation and cross-modal feature fusion, which are enabled by a newly designed coordinate-guided contextual aggregation (CCA) module in the object proposal generation stage, and a multiplex attention (MA) module in the cross-modal feature fusion stage. With the aid of two proposed feature augmentation strategies to alleviate overfitting, we validate that our 3DVG-Transformer outperforms the state-of-the-art methods by a large margin, on two point cloud-based visual grounding datasets, ScanRefer and Nr3D/Sr3D from ReferIt3D, especially for complex scenarios containing multiple objects of the same category.

Dataset & Setup

Data preparation

This codebase is built based on the initial ScanRefer codebase. Please refer to ScanRefer for more data preprocessing details.

1. Download the ScanRefer dataset and unzip it under data/.
2. Downloadand the preprocessed GLoVE embeddings (~990MB) and put them under data/.
3. Download the ScanNetV2 dataset and put (or link) scans/ under (or to) data/scannet/scans/ (Please follow the ScanNet Instructions for downloading the ScanNet dataset).

After this step, there should be folders containing the ScanNet scene data under the data/scannet/scans/ with names like scene0000_00

4. Pre-process ScanNet data. A folder named scannet_data/ will be generated under data/scannet/ after running the following command. Roughly 3.8GB free space is needed for this step:

cd data/scannet/
python batch_load_scannet_data.py

After this step, you can check if the processed scene data is valid by running:

python visualize.py --scene_id scene0000_00

5. (Optional) Pre-process the multiview features from ENet.

• Download: Download the ENet multiview features (~36GB, hdf5 database) and put it under data/scannet/scannet_data/

• Projection:

  a. Download the ENet pretrained weights (1.4MB) and put it under data/ b. Download and decompress the extracted ScanNet frames (~13GB). c. Change the data paths in lib/config.py marked with TODO accordingly. d. Project ENet features from ScanNet frames to point clouds (~36GB, hdf5 database).

python script/project_multiview_features.py --maxpool

Setup

The code is tested on Ubuntu 16.04 LTS & 18.04 LTS with PyTorch 1.2.0 CUDA 10.0 installed.

Please refer to the initial ScanRefer for pointnet2 packages for the newer version (>=1.3.0) of PyTorch.

You could use other PointNet++ implementations for the lower version (<=1.2.0) of PyTorch.

conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch

Install the necessary packages listed out in requirements.txt:

pip install -r requirements.txt

After all packages are properly installed, please run the following commands to compile the CUDA modules for the PointNet++ backbone:

cd lib/pointnet2
python setup.py install

Before moving on to the next step, please don't forget to set the project root path to the CONF.PATH.BASE in lib/config.py.

Usage

Training

To train the 3DVG-Transformer model with multiview features:

python scripts/ScanRefer_train.py --use_multiview --use_normal --batch_size 8 --epoch 200 --lr 0.002 --coslr --tag 3dvg-trans+

settings: XYZ: --use_normal XYZ+RGB: --use_color --use_normal XYZ+Multiview: --use_multiview --use_normal

For more training options (like using preprocessed multiview features), please run scripts/train.py -h.

Evaluation

To evaluate the trained models, please find the folder under outputs/ and run:

python scripts/ScanRefer_eval.py --folder <folder_name> --reference --use_multiview --no_nms --force --repeat 5 --lang_num_max 1

Note that the flags must match the ones set before training. The training information is stored in outputs/<folder_name>/info.json

Note that the results generated by ScanRefer_eval.py may be slightly lower than the test results during training. The main reason is that the results of model testing fluctuate, while the maximum value is reported during training, and we do not use a fixed test seed.

Benchmark Challenge

Note that every user is allowed to submit the test set results of each method only twice, and the ScanRefer benchmark blocks update the test set results of a method for two weeks after a test set submission.

After finishing training the model, please download the benchmark data and put the unzipped ScanRefer_filtered_test.json under data/. Then, you can run the following script the generate predictions:

python benchmark/predict.py --folder <folder_name> --use_color

Note that the flags must match the ones set before training. The training information is stored in outputs/<folder_name>/info.json. The generated predictions are stored in outputs/<folder_name>/pred.json. For submitting the predictions, please compress the pred.json as a .zip or .7z file and follow the instructions to upload your results.

Visualization

To predict the localization results predicted by the trained ScanRefer model in a specific scene, please find the corresponding folder under outputs/ with the current timestamp and run:

python scripts/visualize.py --folder <folder_name> --scene_id <scene_id> --use_color

Note that the flags must match the ones set before training. The training information is stored in outputs/<folder_name>/info.json. The output .ply files will be stored under outputs/<folder_name>/vis/<scene_id>/

In our next version, the heatmap visualization code will be open-sourced in the 3DJCG (CVPR2022, Oral) codebase.

The generated .ply or .obj files could be visualized in software such as MeshLab.

Results

settings: 3D Only (XYZ+RGB): --use_color --use_normal 2D+3D (XYZ+Multiview): --use_multiview --use_normal

Validation Set			Unique	Unique	Multiple	Multiple	Overall	Overall
Methods	Publication	Modality	Acc@0.25	Acc@0.5	Acc@0.25	Acc@0.5	Acc@0.25	Acc@0.5
SCRC	CVPR16	2D	24.03	9.22	17.77	5.97	18.70	6.45
One-Stage	ICCV19	2D	29.32	22.82	18.72	6.49	20.38	9.04
ScanRefer	ECCV2020	3D	67.64	46.19	32.06	21.26	38.97	26.10
TGNN	AAAI2021	3D	68.61	56.80	29.84	23.18	37.37	29.70
InstanceRefer	ICCV2021	3D	77.45	66.83	31.27	24.77	40.23	32.93
SAT	ICCV2021	3D	73.21	50.83	37.64	25.16	44.54	30.14
3DVG-Transformer (ours)	ICCV2021	3D	77.16	58.47	38.38	28.70	45.90	34.47
BEAUTY-DETR	-	3D	-	-	-	-	46.40	-
3DJCG	CVPR2022	3D	78.75	61.30	40.13	30.08	47.62	36.14
3D-SPS	CVPR2022	3D	81.63	64.77	39.48	29.61	47.65	36.43
ScanRefer	ECCV2020	2D + 3D	76.33	53.51	32.73	21.11	41.19	27.40
TGNN	AAAI2021	2D + 3D	68.61	56.80	29.84	23.18	37.37	29.70
InstanceRefer	ICCV2021	2D + 3D	75.72	64.66	29.41	22.99	38.40	31.08
3DVG-Transformer (Ours)	ICCV2021	2D + 3D	81.93	60.64	39.30	28.42	47.57	34.67
3DVG-Transformer+(Ours, this codebase)	-	2D + 3D	83.25	61.95	41.20	30.29	49.36	36.43
MVT-3DVG	CVPR2022	2D + 3D	77.67	66.45	31.92	25.26	40.80	33.26
3DJCG	CVPR2022	2D + 3D	83.47	64.34	41.39	30.82	49.56	37.33
3D-SPS	CVPR2022	2D + 3D	84.12	66.72	40.32	29.82	48.82	36.98

Online Benchmark		Unique	Unique	Multiple	Multiple	Overall	Overall
Methods	Modality	Acc@0.25	Acc@0.5	Acc@0.25	Acc@0.5	Acc@0.25	Acc@0.5
ScanRefer	2D + 3D	68.59	43.53	34.88	20.97	42.44	26.03
TGNN	2D + 3D	68.34	58.94	/33.12	25.26	41.02	32.81
InstanceRefer	2D + 3D	77.82	66.69	34.57	26.88	44.27	35.80
3DVG-Transformer (Ours)	2D + 3D	75.76	55.15	42.24	29.33	49.76	35.12
3DVG-Transformer+(Ours)	2D + 3D	77.33	57.87	43.70	31.02	51.24	37.04

Changelog

2022/04: Update Readme.md.

2022/04: Release the codes of 3DVG-Transformer.

2021/07: 3DVG-Transformer is accepted at ICCV 2021.

2021/06: 3DVG-Transformer+ won the ScanRefer Challenge in the CVPR2021 1st Workshop on Language for 3D Scenes.

2021/04: 3DVG-Transformer+ achieves 1st place in ScanRefer Leaderboard.

Citation

If you use the codes in your work, please kindly cite our work 3DVG-Transformer and the original ScanRefer paper:

@inproceedings{zhao2021_3DVG_Transformer,
    title={{3DVG-Transformer}: Relation modeling for visual grounding on point clouds},
    author={Zhao, Lichen and Cai, Daigang and Sheng, Lu and Xu, Dong},
    booktitle={ICCV},
    pages={2928--2937},
    year={2021}
}

@article{chen2020scanrefer,
    title={{ScanRefer}: 3D Object Localization in RGB-D Scans using Natural Language},
    author={Chen, Dave Zhenyu and Chang, Angel X and Nie{\ss}ner, Matthias},
    pages={202--221},
    journal={ECCV},
    year={2020}
}

Acknowledgement

We would like to thank facebookresearch/votenet for the 3D object detection codebase and erikwijmans/Pointnet2_PyTorch for the CUDA accelerated PointNet++ implementation.

For further acceleration, you could use KD-Tree to accelerate the PointNet++ process.

License

This repository is released under MIT License (see LICENSE file for details).