RefNetV2

In this project, the ScanRefer baseline architecture is modified to take advantage of an improved transformer-inspired object detection. Our proposed architecture is depicted in the visualization below.

The overall aim of this project is to improve the results of the visual grounding task, which aims to localize an object in a 3D point cloud representation of a scene based on a natural language description. For a detailed description of this project, please refer to the report, the poster, and the presentations in the docs directory.

To demonstrate the performance of our approach, some results are presented below.

Usage

Training

The basic settings we used to train the models described in the final report are shown below (we used pre-trained detection modules as mentioned further below).

L6: Object detector with 6 transformer decoder blocks:

python scripts/train.py --no_lang_cls --no_height --batch_size 10 --lr_scheduler plateau \
                        --augment --val_step 3000

L12, wx2: Object detector with 12 transformer decoder blocks and a double width backbone:

python scripts/train.py --no_lang_cls --no_height --batch_size 5 --lr_scheduler plateau \
                        --augment --val_step 3000 --num_decoder_layers 12 --width 2

For additional input features, select appropriate flags (and remove --no_height to include height values):

--use_color --use_normal --use_multiview

To use the language classification proxy loss just remove the --no_lang_cls flag, and to use our proposed multi-ref loss or the VoteNet objectness just set the corresponding flag:

--use_multi_ref_gt --use_votenet_objectness

Pre-trained detector

It is strongly advised to use a pre-trained weight initialization for the transformer-based object detector. You can use the provided weights in Group-Free 3D Object Detection via Transformers via:

--use_pretrained_transformer <path to model>

Or you can pre-train your own detection module with our code using the --no_reference flag. The detector will then be trained on the entire ScanNet training data for detection. To use your own pre-trained detection module as weight initialization for visual grounding, set the flag:

--use_pretrained_det <folder name in outputs>

Visualization

Use the following arguments for the visualize.py script (+ all the arguments required to load the model, e.g. --no_height for models that don't use the hight as input or --num_decoder_layers 12 for the 12 layer detector):

python scripts/visualize.py --folder <path to model> --scene_id <id from val set> \
                            --batch_size 2

Evaluation of our models

Please contact [email protected] or [email protected] in order to receive pre-trained models.

REFNETV2_6L_XYZ - 6 transformer decoder blocks, input: XYZ-coordinates:

python scripts/eval.py --folder <path to model> --reference --no_height --no_lang_cls \
                       --no_nms --force --repeat 5 --batch_size 6

REFNETV2_6L_XYZ_H_N_RGB - 6 transformer decoder blocks, input: XYZ-coordinates, height, normals, RGB:

python scripts/eval.py --folder <path to model> --reference --use_color --use_normal \
                       --no_lang_cls --no_nms --force --repeat 5 --batch_size 6

REFNETV2_6L_XYZ_H_N_M - 6 transformer decoder blocks, input: XYZ-coordinates, height, normals, multiview features:

python scripts/eval.py --folder <path to model> --reference --use_multiview --use_normal \
                       --no_lang_cls --no_nms --force --repeat 5 --batch_size 6

REFNETV2_12L_W2_XYZ - 12 transformer decoder blocks and double backbone with, input: XYZ-coordinates:

python scripts/eval.py --folder <path to model> --reference --no_height --no_lang_cls \
                       --no_nms --force --repeat 5 --num_decoder_layers 12 --width 2 \
                        --batch_size 3

Further information and arguments

python scripts/train.py --help
python scripts/eval.py --help
python scripts/visualize.py --help

Dataset

To setup and prepare the data, please refer to the ScanRefer repository.

References

The project and source code is based on the ScanRefer and the Group-Free-3D project.

@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},
    journal={16th European Conference on Computer Vision (ECCV)},
    year={2020}
}

@inproceedings{dai2017scannet,
    title={Scannet: Richly-annotated 3d reconstructions of indoor scenes},
    author={Dai, Angela and Chang, Angel X and Savva, Manolis and Halber, Maciej and Funkhouser, Thomas and Nie{\ss}ner, Matthias},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    pages={5828--5839},
    year={2017}
}

@article{liu2021,
    title={Group-Free 3D Object Detection via Transformers},
    author={Liu, Ze and Zhang, Zheng and Cao, Yue and Hu, Han and Tong, Xin},
    journal={arXiv preprint arXiv:2104.00678},
    year={2021}
}