Person Re-id in the 3D Space

Thanks for your attention. In this repo, we provide the code for the paper [Parameter-Efficient Person Re-identification in the 3D Space ], published at IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2022.

News

9 Mar 2023 Market-1501 is in 3D. Please check our single 2D to 3D reconstruction work https://github.com/layumi/3D-Magic-Mirror.
29 Sep 2022. I updated Circle loss, parameter count and the latest snapshots trained on 4 datasets, including Market, Duke, CUHK and MSMT, in /snapshots. You can directly test it after dataset preparing.
31 Jul 2021. Circle loss is added. For the fair comparison with circle loss, I re-train almost all the models with a bigger batch size. The results are updated in the latest arXiv version.
30 Oct 2020. I simply modify code on three points to further improve the performance:

More training epochs help; (Since we are trained from scratch)
I replace the dgl to more efficient KNN implementation to accelebrate training; (DGL does not optimize KNN very well, and Matrix Multiplication works quicker. )
For MSMT-17 and Duke, some classes contain too many images, while other categories are under-explored. I apply the stratified sampling (--balance), which takes training samples of each class with equal probability.

You may directly download my generated 3D data of the Market-1501 dataset at [OneDrive] or [GoogleDrive], and therefore you could skip the data preparation part. Just put the datasets in the same folder of the code.

├── 2DMarket\
│   ├── query/  
│   ├── train_all/
│   ├── ...
├── 3DMarket+bg\
│   ├── query/  
│   ├── train_all/
│   ├── ...
├── train.py
├── test.py 
├── ...

Prerequisites

Python 3.6 or 3.7
GPU Memory >= 4G (e.g., GTX1080)
Pytorch = 1.4.0 (Not Latest. Latest version is incompatible, since it changes the C++ interfaces.)
dgl

Install

Here I use the cuda10.1 by default.

conda create --name OG python=3.7
conda activate OG
conda install pytorch=1.4.0 torchvision=0.5.0 cudatoolkit=10.1 -c pytorch
conda install matplotlib requests
conda install -c dglteam dgl-cuda10.1=0.4.3
pip install -r requirements.txt

If you face any error, you may first try to re-install open3d. It helps. And make sure the gcc version is larger than 5.4.0. If you do not have the sudo permission, you may install gcc by conda as follows:

conda install -c brown-data-science gcc          (which is gcc-5.4.0)
gcc -v                                          (to see whether installation is successful)
ln libstdc++.so.6.0.26 libstdc++.so.6            (update lib in /anaconda3/env/OG/lib)
conda install gxx_linux-64
conda install gcc_linux-64

Prepare Data

Download our prepared data. You may directly download my generated 3D data of the Market-1501 dataset at [OneDrive] or [GoogleDrive], and therefore you could totally skip the next data preparation part.
3D Part We first generate the 3D data via the code at https://github.com/layumi/hmr (I modified the code from https://github.com/akanazawa/hmr and added 2D-to-3D color mapping.)

I remove all 3D faces and only keep 3D points positions&RGB to save the storage & loading burden. You can use any text readers (such as vim) to see my generated obj files.

2D Part Download Market-1501, DukeMTMC-reID or MSMT17 and unzip them in the ../

Split the dataset and arrange them in the folder of ID by the following code.

python prepare_market.py # You may need to change the download path. 
python prepare_duke.py
python prepare_MSMT.py

Link the 2DDataset to this dir.

ln -s ../Your_Market/pytorch  ./2DMarket
ln -s ../Your_Duke/pytorch  ./2DDuke
ln -s ../Your_MSMT/pytorch  ./2DMSMT

Training

1. Market-1501

OG-Net 86.82 (69.02)

python train_M.py --batch-size 36 --name Efficient_ALL_Dense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1500_wa0.9_GeM_bn2_class3_amsgrad --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1500  --feature_dims 64,128,256,512   --efficient  --wa --wa_start 0.9 --gem --norm_layer bn2   --amsgrad --class 3

OG-Net + Circle 87.80 (70.56)

python train_M.py --batch-size 36 --name Efficient_ALL_Dense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1500_wa0.9_GeM_bn2_balance_circle_amsgrad_gamma64 --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1500  --feature_dims 64,128,256,512   --efficient  --wa --wa_start 0.9 --gem --norm_layer bn2 --balance  --circle --amsgrad --gamma 64

OG-Net-Small 86.79 (67.92)

python train_M.py --batch-size 36 --name Efficient_ALL_SDense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_wa0.9_GeM_bn2_balance_amsgrad --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 48,96,192,384   --efficient  --wa --wa_start 0.9 --gem --norm_layer bn2 --balance  --amsgrad

OG-Net-Small + Circle 87.38 (70.48)

python train_M.py --batch-size 36 --name Efficient_ALL_SDense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1500_wa0.9_GeM_bn2_balance_circle_amsgrad_gamma64 --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1500  --feature_dims 48,96,192,384   --efficient  --wa --wa_start 0.9 --gem --norm_layer bn2 --balance  --circle --amsgrad --gamma 64

OG-Net-Deep + Circle 88.81 (72.91)

python train_M.py --batch-size 30 --name Market_Efficient_ALL_2SDDense_b30_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_id2_bn_k9_conv2_balance  --id_skip 2 --slim 0.5 --flip --scale  --lrRate 6e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 48,96,96,192,192,384,384  --efficient --k 9  --num_conv 2  --dataset 2DMarket --balance --gem --norm_layer bn2 --circle --amsgrad --gamma 64

1. DukeMTMC-reID

OG-Net-Small 77.33 (57.74)

python train_M.py --batch-size 36 --name reEfficient_Duke_ALL_SDense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_class_GeM_bn2_amsgrad --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 48,96,192,384   --efficient --dataset 2DDuke --class --wa --wa_start 0.9 --gem --norm_layer bn2  --amsgrad

OG-Net-Small + Circle 77.15 (58.51)

python train_M.py --batch-size 36 --name reEfficient_Duke_ALL_SDense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_balance_GeM_bn2_circle_amsgrad --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 48,96,192,384   --efficient --dataset 2DDuke --balance --wa --wa_start 0.9 --gem --norm_layer bn2 --circle --amsgrad

OG-Net 76.53 (57.92)

python train_M.py --batch-size 36 --name reEfficient_Duke_ALL_Dense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_class1_GeM_bn2_amsgrad --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 64,128,256,512   --efficient --dataset 2DDuke --class 1 --wa --wa_start 0.9 --gem --norm_layer bn2 --amsgrad

OG-Net + Circle 78.37 (60.07)

python train_M.py --batch-size 36 --name reEfficient_Duke_ALL_Dense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_balance_GeM_bn2_circle_amsgrad_gamma64 --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 64,128,256,512   --efficient --dataset 2DDuke --balance --wa --wa_start 0.9 --gem --norm_layer bn2 --circle --amsgrad --gamma 64

OG-Net-Deep 76.97 (59.23)

python train_M.py --batch-size 36 --name Duke_Efficient_ALL_2SDDense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_id2_bn_k9_conv2_balance_noCircle  --id_skip 2 --slim 0.5 --flip --scale  --lrRate 6e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 48,96,96,192,192,384,384  --efficient --k 9  --num_conv 2  --dataset 2DDuke --balance --gem --norm_layer bn2 --amsgrad

OG-Net-Deep + Circle 78.50 (60.7)

python train_M.py --batch-size 36 --name Duke_Efficient_ALL_2SDDense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_id2_bn_k9_conv2_balance  --id_skip 2 --slim 0.5 --flip --scale  --lrRate 6e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 48,96,96,192,192,384,384  --efficient --k 9  --num_conv 2  --dataset 2DDuke --balance --gem --norm_layer bn2 --circle --amsgrad --gamma 64

1. CUHK-NP

OG-Net 44.00 (39.28)

python train_M.py --batch-size 36 --name Efficient_CUHK_ALL_Dense_b36_lr8_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_class1_gem_bn2_amsgrad_wd1e-3 --slim 0.5 --flip --scale  --lrRate 8e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1 --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 64,128,256,512    --efficient --dataset 2DCUHK --class 1  --gem --norm_layer bn2  --amsgrad  --wd 1e-3

OG-Net + Circle 48.29 (43.73)

python train_M.py --batch-size 36 --name Efficient_CUHK_ALL_Dense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_class3_gem_bn2_circle_amsgrad_wd1e-3_gamma96 --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 64,128,256,512    --efficient --dataset 2DCUHK --class 3 --gem --norm_layer bn2 --circle --amsgrad --wd 1e-3 --gamma 96

OG-Net-Small 43.07 (38.06)

python train_M.py --batch-size 36 --name Efficient_CUHK_ALL_SDense_b36_lr10_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_gem_bn2_amsgrad_wd1e-3_class1 --slim 0.5 --flip --scale  --lrRate 10e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam   --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 48,96,192,384    --efficient --dataset 2DCUHK --gem --norm_layer bn2  --amsgrad --wd 1e-3  --class 1

OG-Net-Small + Circle 46.43 (41.79)

python train_M.py --batch-size 36 --name Efficient_CUHK_ALL_SDense_b36_lr8_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_balance_gem_bn2_circle_amsgrad_wd1e-3_gamma64 --slim 0.5 --flip --scale  --lrRate 8e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam   --init 768  --cluster xyzrgb  --train_all   --num-epoch 1000  --feature_dims 48,96,192,384    --efficient --dataset 2DCUHK --balance --gem --norm_layer bn2 --circle --amsgrad --wd 1e-3 --gamma 64

OG-Net-Deep 45.71 (41.15)

python train_M.py --batch-size 36 --name CUHK_Efficient_ALL_2SDDense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1500_id2_bn_k9_conv2_class3_Nocircle  --id_skip 2 --slim 0.5 --flip --scale  --lrRate 6e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1500  --feature_dims 48,96,96,192,192,384,384  --efficient --k 9  --num_conv 2  --dataset 2DCUHK --class 3 --gem --norm_layer bn2 --amsgrad

OG-Net-Deep + Circle 49.43 (45.71)

python train_M.py --batch-size 36 --name CUHK_Efficient_ALL_2SDDense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1500_id2_bn_k9_conv2_balance  --id_skip 2 --slim 0.5 --flip --scale  --lrRate 6e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 1500  --feature_dims 48,96,96,192,192,384,384  --efficient --k 9  --num_conv 2  --dataset 2DCUHK --balance --gem --norm_layer bn2 --circle --amsgrad --gamma 64

1. MSMT-17

OG-Net 44.27 (21.57)

python train_M.py --batch-size 36 --name reEfficient_MSMT_ALL_Dense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e600_balance_GeM_bn2_circle_amsgrad_gamma64_Nocircle --slim 0.5 --flip --scale  --lrRate 6e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 600  --feature_dims 64,128,256,512   --efficient --dataset 2DMSMT --balance --wa --wa_start 0.9 --gem --norm_layer bn2  --amsgrad

OG-Net + Circle 45.28 (22.81)

python train_M.py --batch-size 36 --name reEfficient_MSMT_ALL_Dense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e600_balance_GeM_bn2_circle_amsgrad_gamma64 --slim 0.5 --flip --scale  --lrRate 6e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 600  --feature_dims 64,128,256,512   --efficient --dataset 2DMSMT --balance --wa --wa_start 0.9 --gem --norm_layer bn2 --circle --amsgrad --gamma 64

OG-Net-Small 43.84 (21.79)

python train_M.py --batch-size 36 --name reEfficient_MSMT_ALL_SDense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e600_balance_GeM_bn2_circle_amsgrad_gamma64 --slim 0.5 --flip --scale  --lrRate 6e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 600  --feature_dims 48,96,192,384   --efficient --dataset 2DMSMT --balance --wa --wa_start 0.9 --gem --norm_layer bn2 --circle --amsgrad --gamma 64

OG-Net-Small + Circle 42.44 (20.31)

python train_M.py --batch-size 36 --name reEfficient_MSMT_ALL_SDense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e600_class_GeM_bn2_amsgrad --slim 0.5 --flip --scale  --lrRate 6e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 600  --feature_dims 48,96,192,384   --efficient --dataset 2DMSMT --class 1  --wa --wa_start 0.9 --gem --norm_layer bn2  --amsgrad

OG-Net-Deep 44.56 (21.41)

python train_M.py --batch-size 30 --name MSMT_Efficient_ALL_2SDDense_b30_lr4_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e600_id2_bn_k9_conv2_balance_nocircle  --id_skip 2 --slim 0.5 --flip --scale  --lrRate 4e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 600  --feature_dims 48,96,96,192,192,384,384  --efficient --k 9  --num_conv 2  --dataset 2DMSMT --balance --gem --norm_layer bn2 --amsgrad

OG-Net-Deep + Circle 47.32 (24.07)

python train_M.py --batch-size 30 --name MSMT_Efficient_ALL_2SDDense_b30_lr4_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e600_id2_bn_k9_conv2_balance  --id_skip 2 --slim 0.5 --flip --scale  --lrRate 4e-4 --gpu_ids 0 --warm_epoch 10  --erase 0  --droprate 0.7   --use_dense  --bg 1  --adam  --init 768  --cluster xyzrgb  --train_all   --num-epoch 600  --feature_dims 48,96,96,192,192,384,384  --efficient --k 9  --num_conv 2  --dataset 2DMSMT --balance --gem --norm_layer bn2 --circle --amsgrad --gamma 64

Evaluation

Market-1501

python test_M.py  --name  Market_Efficient_ALL_2SDDense_b30_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_id2_bn_k9_conv2_balance

DukeMTMC-reID

python test_M.py  --data 2DDuke --name Duke_Efficient_ALL_2SDDense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1000_id2_bn_k9_conv2_balance

CUHK

python test_M.py  --data 2DCUHK --name CUHK_Efficient_ALL_2SDDense_b36_lr6_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e1500_id2_bn_k9_conv2_balance

MSMT-17

python test_MSMT.py  --name MSMT_Efficient_ALL_2SDDense_b30_lr4_flip_slim0.5_warm10_scale_e0_d7+bg_adam_init768_clusterXYZRGB_e600_id2_bn_k9_conv2_balance

Pre-trained Models

Since OG-Net is really small, I has included trained models in this github repo ./snapshot.

If the model is trained on CUHK, Duke or MSMT, I will add dataset name in the model name, otherwise the model is trained on Market.

[ModelNet Performance]

I add OG-Net code to https://github.com/layumi/dgcnn
Results on ModelNet are 93.3 Top1 Accuracy / 90.5 MeanClass Top1 Accuracy.

Citation

You may cite it in your paper. Thanks a lot.

@article{zheng2022person,
  title={Parameter-Efficient Person Re-identification in the 3D Space},
  author={Zheng, Zhedong and Wang, Xiaohan and Zheng, Nenggan and Yang, Yi},
  journal={IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},
  doi={10.1109/TNNLS.2022.3214834},
  note={\mbox{doi}:\url{10.1109/TNNLS.2022.3214834}},
  year={2022}
}

Related Work

We thank the great works of hmr, DGL, DGCNN and PointNet++. You may check their code at

The baseline models used in the paper are modified from:

https://github.com/layumi/Person_reID_baseline_pytorch

Acknowledge

I would like to thank the helpful comments and suggestions from Yaxiong Wang, Yuhang Ding, Qian Liu, Chuchu Han, Tianqi Tang, Zonghan Wu and Qipeng Guo.

Name		Name	Last commit message	Last commit date
Latest commit History 114 Commits
imgs		imgs
pointnet2_ops_lib		pointnet2_ops_lib
snapshot		snapshot
.gitignore		.gitignore
DGCNN.py		DGCNN.py
KNNGraphE.py		KNNGraphE.py
LICENSE		LICENSE
README.md		README.md
circle_loss.py		circle_loss.py
evaluate_gpu.py		evaluate_gpu.py
gated_gcn_layer.py		gated_gcn_layer.py
lookahead.py		lookahead.py
market3d.py		market3d.py
model.py		model.py
model_efficient.py		model_efficient.py
model_efficient2.py		model_efficient2.py
pointnet2_model.py		pointnet2_model.py
prepare_MSMT.py		prepare_MSMT.py
prepare_duke.py		prepare_duke.py
prepare_market.py		prepare_market.py
ptflops.py		ptflops.py
requirements.txt		requirements.txt
sitemap.xml		sitemap.xml
swa_utils.py		swa_utils.py
test_M.py		test_M.py
test_MSMT.py		test_MSMT.py
test_P.py		test_P.py
test_vip.py		test_vip.py
train_M.py		train_M.py
utils.py		utils.py

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

Person Re-id in the 3D Space

News

Prerequisites

Install

Prepare Data

Training

Evaluation

Pre-trained Models

[ModelNet Performance]

Citation

Related Work

Acknowledge

About

Releases

Sponsor this project

Packages

Languages

License

layumi/person-reid-3d

Folders and files

Latest commit

History

Repository files navigation

Person Re-id in the 3D Space

News

Prerequisites

Install

Prepare Data

Training

Evaluation

Pre-trained Models

[ModelNet Performance]

Citation

Related Work

Acknowledge

About

Topics

Resources

License

Stars

Watchers

Forks

Releases

Sponsor this project

Packages 0

Languages

Packages