Yinyu Nie, Angela Dai, Xiaoguang Han, Matthias Nießner

in CVPR 2023

3D Scene Generation

Single View Reconstruction

Input	Pred	Input	Pred

Our codebase is developed under Ubuntu 20.04 with PyTorch 1.12.1.

1. We recommend to use conda to deploy our environment by

   cd ScenePriors
   conda create env -f environment.yml
   conda activate sceneprior
   

2. Install Fast Transformers by

   cd external/fast_transformers
   python setup.py build_ext --inplace
   cd ../..
   

3. Please follow link to install the prerequisite libraries for PyTorch3D. Then install PyTorch3D from our local clone by

   cd external/pytorch3d
   pip install -e .
   cd ../..
   

   Note: After installed all prerequisite libraries in link, please do not install prebuilt binaries for PyTorch3D.

1. Apply & Download the 3D-Front dataset and link them to the local directory as follows:

   datasets/3D-Front/3D-FRONT
   datasets/3D-Front/3D-FRONT-texture
   datasets/3D-Front/3D-FUTURE-model
   

2. Render 3D-Front scenes following my rendering pipeline and link the rendering results (in renderings folder) to

   datasets/3D-Front/3D-FRONT_renderings_improved_mat
   

   Note: you can comment out bproc.renderer.enable_depth_output(activate_antialiasing=False) in render_dataset_improved_mat.py since we do not need depth information.

3. Preprocess 3D-Front data by

   python utils/threed_front/1_process_viewdata.py --room_type ROOM_TYPE --n_processes NUM_THREADS
   python utils/threed_front/2_get_stats.py --room_type ROOM_TYPE
   

   • The processed data for training are saved in datasets/3D-Front/3D-FRONT_samples.
   • We also parsed and extracted the 3D-Front data for visualization into datasets/3D-Front/3D-FRONT_scenes.
   • ROOM_TYPE can be 'bed'(bedroom) or 'living'(living room).
   • You can set NUM_THREADS to your CPU core number for parallel processing.

4. Visualize processed data for verification by (optional)

   python utils/threed_front/vis/vis_gt_sample.py --scene_json SCENE_JSON_ID --room_id ROOM_ID --n_samples N_VIEWS 
   

   • SCENE_JSON_ID is the ID of a scene, e,g, 6a0e73bc-d0c4-4a38-bfb6-e083ce05ebe9.
   • ROOM_ID is the room ID in this scene, e.g., MasterBedroom-2679.
   • N_VIEWS is the number views to visualize., e.g. 12.

   If everything goes smooth, there will pop five visualization windows as follows.

RGB	Semantics	Instances	3D Box Projections	CAD Models (view #1)

Note: X server is required for visualization.

1. Apply and download ScanNet into datasets/ScanNet/scans. Since we need 2D data, the *.sens should also be downloaded for each scene.

2. Extract *.sens files to obtain RGB/semantics/instance/camera pose frame data by

   python utils/scannet/1_unzip_sens.py
   

   Then the folder structure in each scene looks like:

   ./scene*
   |--color (folder)
   |--instance-filt (folder)
   |--intrinsic (folder)
   |--label-filt (folder)
   |--pose (folder)
   |--scene*.aggregation.json
   |--scene*.sens
   |--scene*.txt
   |--scene*_2d-instance.zip
   ...
   |--scene*_vh_clean_2.ply
   

3. Process ScanNet data by

   python utils/scannet/2_process_viewdata.py
   

   The processed data will be saved in datasets/ScanNet/ScanNet_samples.

4. Visualize the processed data by(optional)

   python utils/scannet/vis/vis_gt.py --scene_id SCENE_ID --n_samples N_SAMPLES
   

   • SCENE_ID is the scene ID in scannet, e.g., scene0000_00
   • N_SAMPLES is the number of views to visualize, e.g., 6

   If everything goes smooth, it will pop out five visualization windows like

RGB	Semantics	Instances	3D Box Projections	3D Boxes (view #3)

Note: X server is required for visualization.

Note: we use SLURM to manage multi-GPU training. For backend setting, please check slurm_jobs.

Here we use bedroom data as an example. Training on living rooms is the same.

1. Start layout pretraining by

   python main.py \
       start_deform=False \
       resume=False \
       finetune=False \
       weight=[] \
       distributed.num_gpus=4 \
       data.dataset=3D-Front \
       data.split_type=bed \
       data.n_views=20 \
       data.aug=False \
       device.num_workers=32 \
       train.batch_size=128 \
       train.epochs=800 \
       train.freeze=[] \
       scheduler.latent_input.milestones=[400] \
       scheduler.generator.milestones=[400] \
       log.if_wandb=True \
       exp_name=pretrain_3dfront_bedroom
   

   The network weight will be saved in outputs/3D-Front/train/YEAR-MONTH-DAY/HOUR-MINUTE-SECOND/model_best.pth.
2. Shape training - We start shape training after the layout training converged. Please replace the weight keyword below with the pretrained weight path.

   python main.py \
       start_deform=True \
       resume=False \
       finetune=True \
       weight=['outputs/3D-Front/train/YEAR-MONTH-DAY/HOUR-MINITE-SECOND/model_best.pth'] \
       distributed.num_gpus=4 \
       data.dataset=3D-Front \
       data.n_views=20 \
       data.aug=False \
       data.downsample_ratio=4 \
       device.num_workers=16 \
       train.batch_size=16 \
       train.epochs=500 \
       train.freeze=[] \
       scheduler.latent_input.milestones=[300] \
       scheduler.generator.milestones=[300] \
       log.if_wandb=True \
       exp_name=train_3dfront_bedroom
   

   Still, the refined network weight will be saved in outputs/3D-Front/train/YEAR-MONTH-DAY/HOUR-MINUTE-SECOND/model_best.pth.

1. Start layout pretraining by

   python main.py \
       start_deform=False \
       resume=False \
       finetune=False \
       weight=[] \
       distributed.num_gpus=4 \
       data.dataset=ScanNet \
       data.split_type=all \
       data.n_views=40 \
       data.aug=True \
       device.num_workers=32 \
       train.batch_size=64 \
       train.epochs=500 \
       train.freeze=[] \
       scheduler.latent_input.milestones=[500] \
       scheduler.generator.milestones=[500] \
       log.if_wandb=True \
       exp_name=pretrain_scannet
   

   The network weight will be saved in outputs/ScanNet/train/YEAR-MONTH-DAY/HOUR-MINUTE-SECOND/model_best.pth.

2. Shape training - We start shape training after the layout training converged. Please replace the weight keyword below with the pretrained weight path.

   python main.py \
       start_deform=True \
       resume=False \
       finetune=True \
       weight=['outputs/ScanNet/train/YEAR-MONTH-DAY/HOUR-MINUTE-SECOND/model_best.pth'] \
       distributed.num_gpus=4 \
       data.dataset=ScanNet \
       data.split_type=all \
       data.n_views=40 \
       data.downsample_ratio=4 \
       data.aug=True \
       device.num_workers=8 \
       train.batch_size=8 \
       train.epochs=500 \
       train.freeze=[] \
       scheduler.latent_input.milestones=[300] \
       scheduler.generator.milestones=[300] \
       log.if_wandb=True \
       exp_name=train_scannet

Still, the refined network weight will be saved in outputs/ScanNet/train/YEAR-MONTH-DAY/HOUR-MINUTE-SECOND/model_best.pth.

Please replace the keyword weight below with your trained weight path.

1. Scene Generation (with 3D-Front)

   python main.py \
      mode=generation \
      start_deform=True \
      data.dataset=3D-Front \
      finetune=True \
      weight=outputs/ScanNet/train/YEAR-MONTH-DAY/HOUR-MINUTE-SECOND/model_best.pth \
      generation.room_type=bed \
      data.split_dir=splits \
      data.split_type=bed \
      generation.phase=generation
   

   The generated scenes will be saved in outputs/3D-Front/generation/YEAR-MONTH-DAY/HOUR-MINUTE-SECOND.

2. Single view reconstruction (with ScanNet). Since this process involves test-time optimization, it would be very slow. Here we test in parallel by dividing the whole test set into batch_num batches. You should run this script multiple times to finish the whole testing, where for each script, you should set an individual batch_id number, batch_id=0,1,...,batch_num-1. If you not want to run in parallel, you can keep the default setting as below.

   python main.py \
       mode=demo \
       start_deform=True \
       finetune=True \
       data.n_views=1 \
       data.dataset=ScanNet \
       data.split_type=all \
       weight=outputs/ScanNet/train/YEAR-MONTH-DAY/HOUR-MINUTE-SECOND/model_best.pth \
       optimizer.method=RMSprop \
       optimizer.lr=0.01 \
       scheduler.latent_input.milestones=[1200] \
       scheduler.latent_input.gamma=0.1 \
       demo.epochs=2000 \
       demo.batch_id=0 \
       demo.batch_num=1 \
       log.print_step=100 \
       log.if_wandb=False \
   

   The results will be saved in outputs/ScanNet/demo/output.

3. Similarly, you can do single view reconstruction with 3D-Front as well.

   python main.py \
       mode=demo \
       start_deform=True \
       finetune=True \
       data.n_views=1 \
       data.dataset=3D-Front \
       data.split_type=bed \
       weight=outputs/3D-Front/train/2022-09-06/02-37-24/model_best.pth \
       optimizer.method=RMSprop \
       optimizer.lr=0.01 \
       scheduler.latent_input.milestones=[1200] \
       scheduler.latent_input.gamma=0.1 \
       demo.epochs=2000
       demo.batch_id=0 \
       demo.batch_num=1 \
       log.print_step=100 \
       log.if_wandb=False
   

   The results will be saved in outputs/3D-Front/demo/output.

Note: you may need X-server to showcase the visualization windows from VTK.

1. Scene Generation (with 3D-Front).

   python utils/threed_front/vis/render_pred.py --pred_file outputs/3D-Front/generation/YEAR-MONTH-DAY/HOUR-MINUTE-SECOND/vis/bed/sample_X_X.npz [--use_retrieval]
   

2. Single-view Reconstruction (with ScanNet)

   python utils/scannet/vis/vis_prediction_scannet.py --dump_dir demo/ScanNet/output --sample_name all_sceneXXXX_XX_XXXX 
   

3. Single-view Reconstruction (with 3D-Front)

   python utils/threed_front/vis/vis_svr.py --dump_dir demo/3D-Front/output --sample_name [FILENAME IN dump_dir]
   

If you find our work is helpful, please cite

@InProceedings{Nie_2023_CVPR,
    author    = {Nie, Yinyu and Dai, Angela and Han, Xiaoguang and Nie{\ss}ner, Matthias},
    title     = {Learning 3D Scene Priors With 2D Supervision},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {792-802}
}