If you find this work or code useful, please cite our paper and give this repo a star:

@InProceedings{cao2024pasco,
      title={PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness}, 
      author={Anh-Quan Cao and Angela Dai and Raoul de Charette},
      year={2024},
      booktitle = {CVPR}
}

• News
• 1. Installation
• 2. Data
  • 2.1. Semantic KITTI
  • 2.2. SSCBench-KITTI360
  • 2.3. Robo3D
• 3. Panoptic labels generation
  • 3.1. Semantic KITTI
  • 3.2. SSCBench-KITTI360
• 4. Training and evaluation
  • 4.1. Semantic KITTI
    • 4.1.1 Extract point features
    • 4.1.2 Training
    • 4.1.3 Evaluation
  • 4.2. SSCBench-KITTI360
    • 4.2.1 Training
    • 4.2.2 Evaluation
  • 4.3. Robo3D - SemanticKITTI-C
    • 4.3.1 Extract point features
    • 4.3.2 Evaluation
• 5. Visualization
• Acknowledgment

• 25/03/2026: One step towards generalized unified occupnay prediction with OccAny: Generalized Unconstrained Urban 3D Occupancy (CVPR'26)
• 02/08/2024: Code for evaluation on Robo3D has been released.
• 30/07/2024: We added the training, evaluation, and checkpoints for PaSCo on SSCBench-KITTI360.
• 23/07/2024: The training/evaluation code and the checkpoint for PaSCo on SemanticKITTI has been released.
• 25/06/2024: Added visualization code.
• 10/06/2024: Training and evaluation code for PaSCo w/o MIMO has been released.
• 06/04/2024: Dataset download instructions and label generation code for SemanticKITTI are now available.
• 04/04/2024: PaSCo has been accepted as Oral paper at CVPR 2024 (0.8% = 90/11,532).
• 05/12/2023: Paper released on arXiv! Code will be released soon! Please watch this repo for updates.

1. Download the source code with git

   git clone https://github.com/astra-vision/PaSCo.git
   

2. Create conda environment:

   conda create -y -n pasco python=3.9
   conda activate pasco
   

3. Install pytorch 1.13.0

   pip install torch==1.13.0+cu117 torchvision==0.14.0+cu117 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu117
   

4. Install Minkowski Engine v0.5.4

5. Install pytorch_lightning 1.9.0 with torchmetrics 1.4.0.post0

   pip install --no-cache-dir pytorch_lightning==1.9.0
   

6. Install the additional dependencies:

   cd PaSCo/
   pip install -r requirements.txt
   

7. Install pytorch-scatter with torch 1.13.0 and CUDA 11.7:

   pip install torch-scatter -f https://data.pyg.org/whl/torch-1.13.0+cu117.html
   

8. Install PaSCo

   pip install -e ./
   

Please download the following data into a folder e.g. /gpfsdswork/dataset/SemanticKITTI and unzip:

• Semantic Scene Completion dataset v1.1 (SemanticKITTI voxel data (700 MB)) from SemanticKITTI website.

• Point-wise semantic labels (SemanticKITTI label data (179 MB)) from SemanticKITTI website.

• KITTI Odometry Benchmark calibration data (Download odometry data set (calibration files, 1 MB)).

• KITTI Odometry Benchmark Velodyne data (Download odometry data set (velodyne laser data, 80 GB)).

• The dataset folder at /gpfsdswork/dataset/SemanticKITTI should have the following structure:

  └── /gpfsdswork/dataset/SemanticKITTI
    └── dataset
      └── sequences
  

Please download the following data:

• The SSCBench-KITTI360 preprocess.sqf from SSCBench-KITTI360 official github and unsquash it into a folder e.g. /gpfsdswork/dataset/SSCBench-KITTI-360. I also uploaded the file preprocess.sqf here as backup.

• The KITTI-360 Raw Velodyne Scans (119G) from KITTI-360 download page and put into folder e.g. /gpfsdswork/dataset/KITTI-360.

• The folder /gpfsdswork/dataset/KITTI-360 should have the following structure:

  /gpfsdswork/dataset/KITTI-360
  └── data_3d_raw
      ├── 2013_05_28_drive_0000_sync
      ├── 2013_05_28_drive_0002_sync
      ├── 2013_05_28_drive_0003_sync
      ├── 2013_05_28_drive_0004_sync
      ├── 2013_05_28_drive_0005_sync
      ├── 2013_05_28_drive_0006_sync
      ├── 2013_05_28_drive_0007_sync
      └── 2013_05_28_drive_0009_sync
  

• The folder /gpfsdswork/dataset/SSCBench-KITTI-360 should have the following structure:

  /gpfsdswork/dataset/SSCBench-KITTI-360/
  ├── labels                        
  │   ├── 2013_05_28_drive_0000_sync
  │   ├── 2013_05_28_drive_0002_sync
  │   ├── 2013_05_28_drive_0003_sync
  │   ├── 2013_05_28_drive_0004_sync
  │   ├── 2013_05_28_drive_0005_sync
  │   ├── 2013_05_28_drive_0006_sync
  │   ├── 2013_05_28_drive_0007_sync
  │   ├── 2013_05_28_drive_0009_sync
  │   └── 2013_05_28_drive_0010_sync
  ├── labels_half                   
  │   ├── 2013_05_28_drive_0000_sync
  │   ├── 2013_05_28_drive_0002_sync
  │   ├── 2013_05_28_drive_0003_sync
  │   ├── 2013_05_28_drive_0004_sync
  │   ├── 2013_05_28_drive_0005_sync
  │   ├── 2013_05_28_drive_0006_sync
  │   ├── 2013_05_28_drive_0007_sync  
  │   ├── 2013_05_28_drive_0009_sync
  │   └── 2013_05_28_drive_0010_sync
  ├── README            
  └── unified                         
  └── labels
  

1. Please download the SemanticKITTI-C following the official instruction and put in a folder e.g. /gpfsdswork/dataset/SemanticKITTI-C.
2. The folder /gpfsdswork/dataset/SemanticKITTI-C should have the following structure:

   /gpfsdswork/dataset/SemanticKITTI-C
   ├── beam_missing
   ├── cross_sensor
   ├── crosstalk
   ├── fog
   ├── incomplete_echo
   ├── motion_blur
   ├── snow
   └── wet_ground
   

1. Create a folder to store preprocess data for Semantic KITTI dataset e.g. /lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti .
2. Execute the command below to generate panoptic labels, or move to the next step to directly download the pre-generated labels:

   cd PaSCo/
   python label_gen/gen_instance_labels.py \
       --kitti_config=pasco/data/semantic_kitti/semantic-kitti.yaml \
       --kitti_root=/gpfsdswork/dataset/SemanticKITTI \
       --kitti_preprocess_root=/lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti \
       --n_process=10
   

3. You can download the generated panoptic labels for Semantic KITTI:
   1. Go to the preprocess folder for KITTI:

      cd /lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti
      

   2. Download the compressed file:

      wget https://github.com/astra-vision/PaSCo/releases/download/v0.0.1/kitti_instance_label_v2.tar.gz
      

   3. Extract the file:

      tar xvf kitti_instance_label_v2.tar.gz
      

4. Your folder structure should look as follows:

   /lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti
   └── instance_labels_v2
       ├── 00
       ├── 01
       ├── 02
       ├── 03
       ├── 04
       ├── 05
       ├── 06
       ├── 07
       ├── 08
       ├── 09
       └── 10
   

1. Create a folder to store preprocess data for SSCBench-KITTI360 dataset e.g. /lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti360 .
2. Execute the command below to generate panoptic labels (took approximately 8.5 hours to process 12464 scans using 10 processes), or move to the next step to directly download the pre-generated labels:

   cd PaSCo/
   python label_gen/gen_instance_labels_kitti360.py \
       --kitti360_label_root=/gpfsdswork/dataset/SSCBench-KITTI-360 \
       --kitti360_preprocess_root=/lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti360 \
       --n_process=10
   

3. You can download the generated panoptic labels for SSCBench-KITTI360:
   1. Go to the preprocess folder for KITTI360:

      cd /lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti360
      

   2. Create a folder to store the instance label and cd into it:

      mkdir instance_labels_v2
      cd instance_labels_v2
      

   3. Run the following commands to download the compressed files into it:

      wget https://github.com/astra-vision/PaSCo/releases/download/v0.1.0/2013_05_28_drive_0000_sync.tar.gz
      wget https://github.com/astra-vision/PaSCo/releases/download/v0.1.0/2013_05_28_drive_0002_sync.tar.gz
      wget https://github.com/astra-vision/PaSCo/releases/download/v0.1.0/2013_05_28_drive_0003_sync.tar.gz
      wget https://github.com/astra-vision/PaSCo/releases/download/v0.1.0/2013_05_28_drive_0004_sync.tar.gz
      wget https://github.com/astra-vision/PaSCo/releases/download/v0.1.0/2013_05_28_drive_0005_sync.tar.gz
      wget https://github.com/astra-vision/PaSCo/releases/download/v0.1.0/2013_05_28_drive_0006_sync.tar.gz
      wget https://github.com/astra-vision/PaSCo/releases/download/v0.1.0/2013_05_28_drive_0007_sync.tar.gz
      wget https://github.com/astra-vision/PaSCo/releases/download/v0.1.0/2013_05_28_drive_0009_sync.tar.gz
      wget https://github.com/astra-vision/PaSCo/releases/download/v0.1.0/2013_05_28_drive_0010_sync.tar.gz
      

   4. Extract all the downloaded files:

      tar xvf *.tar.gz
      

4. Your folder structure with the instance labels should look as follows:

   /lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti360
   └── instance_labels_v2
       ├── 2013_05_28_drive_0000_sync
       ├── 2013_05_28_drive_0002_sync
       ├── 2013_05_28_drive_0003_sync
       ├── 2013_05_28_drive_0004_sync
       ├── 2013_05_28_drive_0005_sync
       ├── 2013_05_28_drive_0006_sync
       ├── 2013_05_28_drive_0007_sync
       ├── 2013_05_28_drive_0009_sync
       └── 2013_05_28_drive_0010_sync
   

1. Install WaffleIron in a separate conda environment:

   conda create -y -n waffleiron 
   conda activate waffleiron
   pip install pyYAML==6.0 tqdm==4.63.0 scipy==1.8.0 torch==1.11.0 tensorboard==2.8.0
   cd PaSCo/WaffleIron_mod
   pip install -e ./
   

2. Run the following command to extract point features from the pretrained WaffleIron model (require 10883Mb GPU memory) pretrained on SemanticKITTI. The extracted features will be stored in the result_folder:

   cd PaSCo/WaffleIron_mod
   python extract_point_features.py \
   --path_dataset /gpfsdswork/dataset/SemanticKITTI \
   --ckpt pretrained_models/WaffleIron-48-256__kitti/ckpt_last.pth \
   --config configs/WaffleIron-48-256__kitti.yaml \
   --result_folder /lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti/waffleiron_v2 \
   --phase val \
   --num_workers 3 \
   --num_votes 10 \
   --batch_size 2
   

1. Change the dataset_preprocess_root and dataset_root of the training command below to the preprocess and raw data folder respectively.

2. The log_dir is the folder to store the training logs and checkpoints.

3. Train PaSCo with MIMO (i.e. 1 subnet) using the following command with a batchsize of 2 on 2 V100-32G GPUs (1 item per GPU):

   cd PaSCo/
   python scripts/train.py --bs=2 --n_gpus=2 \
         --dataset_preprocess_root=/lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti \
         --dataset_root=/gpfsdswork/dataset/SemanticKITTI \
         --log_dir=logs \
         --exp_prefix=pasco_single --lr=1e-4 --seed=0 \
         --data_aug=True --max_angle=30.0 --translate_distance=0.2 \
         --enable_log=True \
         --n_infers=1
   

4. Train PaSCo (3 subnets) by setting --n_infers=3 (number of subnets = 3) with batchsize of 2 on 2 A100-80G GPUs (1 items per GPU):

   cd PaSCo/
   python scripts/train.py --bs=2 --n_gpus=2 \
         --dataset_preprocess_root=/lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti \
         --dataset_root=/gpfsdswork/dataset/SemanticKITTI \
         --log_dir=logs \
         --exp_prefix=pasco_single --lr=1e-4 --seed=0 \
         --data_aug=True --max_angle=30.0 --translate_distance=0.2 \
         --enable_log=True \
         --n_infers=3
   

1. Download the pretrained checkpoint of PaSCO or PaSCO without MIMO and put it into ckpt folder or use your trained checkpoint.

   cd ckpt
   wget https://github.com/astra-vision/PaSCo/releases/download/v0.1.0/pasco.ckpt
   wget https://github.com/astra-vision/PaSCo/releases/download/v0.1.0/pasco_single.ckpt
   

2. Evaluate PaSCo without MIMO on 1 V100-32G GPUs (1 item per GPU). ckpt/pasco_single.ckpt is the path to the downloaded checkpoint:

   python scripts/eval.py --n_infers=1 --model_path=ckpt/pasco_single.ckpt \
         --dataset_preprocess_root=/lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti \
         --dataset_root=/gpfsdswork/dataset/SemanticKITTI 
   

3. Evaluate PaSCo on 1 V100-32G GPUs (1 item per GPU). ckpt/pasco.ckpt is the path to the downloaded checkpoint:

   python scripts/eval.py --n_infers=3 --model_path=ckpt/pasco.ckpt \
         --dataset_preprocess_root=/lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti \
         --dataset_root=/gpfsdswork/dataset/SemanticKITTI 
   

4. Output looks like following:

   • PaSCo without MIMO:

   =====================================
   method, P, R, IoU, mIoU, All PQ dagger, All PQ, All SQ, All RQ, Thing PQ, Thing SQ, Thing RQ, Stuff PQ, Stuff SQ, Stuff RQ
   subnet 0, 86.41, 57.98, 53.13, 29.15, 26.33, 15.71, 53.82, 24.27, 12.27, 47.18, 18.86, 18.21, 58.65, 28.20
   ensemble, 86.41, 57.98, 53.13, 29.15, 26.33, 15.71, 53.82, 24.27, 12.27, 47.18, 18.86, 18.21, 58.65, 28.20
   =====================================
   ==> pq
   method, car, bicycle, motorcycle, truck, other-vehicle, person, bicyclist, motorcyclist, road, parking, sidewalk, other-ground, building, fence, vegetation, trunk, terrain, pole, traffic-sign
   subnet 0, 27.53, 6.21, 16.86, 34.27, 9.77, 3.53, 0.00, 0.00, 74.51, 26.63, 39.70, 0.54, 4.10, 4.64, 6.87, 3.80, 29.58, 7.68, 2.28
   ensemble, 27.53, 6.21, 16.86, 34.27, 9.77, 3.53, 0.00, 0.00, 74.51, 26.63, 39.70, 0.54, 4.10, 4.64, 6.87, 3.80, 29.58, 7.68, 2.28
   ==> sq
   method, car, bicycle, motorcycle, truck, other-vehicle, person, bicyclist, motorcyclist, road, parking, sidewalk, other-ground, building, fence, vegetation, trunk, terrain, pole, traffic-sign
   subnet 0, 69.83, 57.87, 64.56, 65.42, 59.95, 59.80, 0.00, 0.00, 75.74, 63.11, 58.65, 52.57, 56.08, 55.89, 52.51, 58.07, 62.12, 55.01, 55.42
   ensemble, 69.83, 57.87, 64.56, 65.42, 59.95, 59.80, 0.00, 0.00, 75.74, 63.11, 58.65, 52.57, 56.08, 55.89, 52.51, 58.07, 62.12, 55.01, 55.42
   ==> rq
   method, car, bicycle, motorcycle, truck, other-vehicle, person, bicyclist, motorcyclist, road, parking, sidewalk, other-ground, building, fence, vegetation, trunk, terrain, pole, traffic-sign
   subnet 0, 39.43, 10.73, 26.11, 52.38, 16.29, 5.91, 0.00, 0.00, 98.38, 42.19, 67.69, 1.04, 7.31, 8.31, 13.07, 6.54, 47.61, 13.96, 4.11
   ensemble, 39.43, 10.73, 26.11, 52.38, 16.29, 5.91, 0.00, 0.00, 98.38, 42.19, 67.69, 1.04, 7.31, 8.31, 13.07, 6.54, 47.61, 13.96, 4.11
   [2.621915578842163, 0.8142204284667969, 0.8685343265533447, 0.7775185108184814, 0.9801337718963623, 0.6943247318267822]
   inference time:  0.7034459143364459
   [0.004038333892822266, 0.003854036331176758, 0.005398988723754883, 0.003660440444946289, 0.004451274871826172, 0.003663778305053711]
   ensemble time:  0.004062994399293342
   Uncertainty threshold:  0.5
   =====================================
   method, ins ece, ins nll, ssc nonempty ece, ssc empty ece, ssc nonempty nll, ssc empty nll,  count, inference time
   subnet 0,  0.6235, 4.6463, 0.0911, 0.0357, 0.7075, 0.9657, 11702, 0.00
   ensemble,  0.6235, 4.6463, 0.0911, 0.0357, 0.7075, 0.9657, 11702, 0.00
   allocated 8895.119325153375
   

   • PaSCo:

   =====================================
   method, P, R, IoU, mIoU, All PQ dagger, All PQ, All SQ, All RQ, Thing PQ, Thing SQ, Thing RQ, Stuff PQ, Stuff SQ, Stuff RQ
   subnet 0, 77.41, 65.46, 54.96, 27.55, 25.17, 14.67, 56.88, 23.18, 10.80, 52.60, 17.42, 17.49, 59.99, 27.38
   subnet 1, 79.38, 63.54, 54.54, 27.36, 25.56, 14.51, 53.75, 22.78, 10.80, 46.09, 17.11, 17.20, 59.33, 26.90
   subnet 2, 75.10, 67.29, 55.01, 27.79, 25.54, 14.98, 53.23, 23.51, 11.74, 53.30, 18.49, 17.33, 53.18, 27.17
   ensemble, 83.59, 62.10, 55.35, 29.54, 30.61, 16.38, 55.41, 25.24, 13.49, 47.20, 20.93, 18.49, 61.38, 28.37
   =====================================
   ==> pq
   method, car, bicycle, motorcycle, truck, other-vehicle, person, bicyclist, motorcyclist, road, parking, sidewalk, other-ground, building, fence, vegetation, trunk, terrain, pole, traffic-sign
   subnet 0, 25.31, 7.34, 14.46, 28.97, 7.78, 2.27, 0.24, 0.00, 73.71, 21.03, 35.21, 0.78, 6.46, 4.35, 10.70, 2.65, 29.88, 6.09, 1.55
   subnet 1, 23.68, 4.74, 9.70, 37.66, 7.98, 2.62, 0.00, 0.00, 73.47, 18.59, 35.12, 0.68, 6.72, 3.65, 10.17, 3.25, 29.53, 5.67, 2.38
   subnet 2, 24.04, 7.17, 16.35, 36.08, 8.14, 1.92, 0.24, 0.00, 73.99, 22.03, 34.39, 0.00, 6.30, 2.59, 10.16, 3.24, 29.58, 5.95, 2.39
   ensemble, 27.44, 8.70, 16.79, 42.71, 9.55, 2.70, 0.00, 0.00, 75.65, 25.51, 36.83, 0.90, 6.27, 0.32, 11.13, 4.31, 31.61, 8.15, 2.70
   ==> sq
   method, car, bicycle, motorcycle, truck, other-vehicle, person, bicyclist, motorcyclist, road, parking, sidewalk, other-ground, building, fence, vegetation, trunk, terrain, pole, traffic-sign
   subnet 0, 66.64, 58.42, 62.35, 60.01, 59.58, 59.09, 54.75, 0.00, 75.02, 62.31, 57.57, 69.60, 55.13, 55.76, 52.73, 59.12, 61.43, 54.35, 56.92
   subnet 1, 66.31, 58.21, 63.06, 63.01, 59.65, 58.46, 0.00, 0.00, 74.97, 62.37, 57.99, 66.12, 55.11, 55.67, 52.16, 57.16, 61.72, 54.83, 54.52
   subnet 2, 66.35, 58.97, 65.16, 63.13, 60.61, 56.54, 55.67, 0.00, 75.26, 61.39, 57.35, 0.00, 54.78, 55.43, 52.69, 57.71, 61.29, 54.21, 54.87
   ensemble, 69.20, 59.77, 65.81, 63.30, 61.22, 58.33, 0.00, 0.00, 76.99, 63.37, 59.20, 72.50, 56.39, 62.66, 52.83, 57.40, 62.94, 54.98, 55.88
   ==> rq
   method, car, bicycle, motorcycle, truck, other-vehicle, person, bicyclist, motorcyclist, road, parking, sidewalk, other-ground, building, fence, vegetation, trunk, terrain, pole, traffic-sign
   subnet 0, 37.97, 12.56, 23.19, 48.28, 13.06, 3.85, 0.44, 0.00, 98.25, 33.74, 61.16, 1.12, 11.72, 7.81, 20.29, 4.48, 48.63, 11.21, 2.73
   subnet 1, 35.71, 8.14, 15.38, 59.77, 13.38, 4.49, 0.00, 0.00, 98.00, 29.81, 60.56, 1.03, 12.20, 6.55, 19.49, 5.69, 47.85, 10.33, 4.36
   subnet 2, 36.23, 12.16, 25.10, 57.14, 13.43, 3.40, 0.44, 0.00, 98.32, 35.88, 59.97, 0.00, 11.49, 4.67, 19.29, 5.61, 48.26, 10.97, 4.35
   ensemble, 39.65, 14.55, 25.51, 67.47, 15.60, 4.63, 0.00, 0.00, 98.25, 40.25, 62.21, 1.23, 11.11, 0.52, 21.08, 7.52, 50.23, 14.83, 4.83
   [2.5787551403045654, 1.415881872177124, 1.4355676174163818, 1.3622872829437256, 1.3630497455596924, 1.491441011428833]
   inference time:  1.1929051064741991
   [0.03452730178833008, 0.03274846076965332, 0.03339242935180664, 0.03439188003540039, 0.03410673141479492, 0.03477001190185547]
   ensemble time:  0.03270599180123144
   Uncertainty threshold:  0.5
   =====================================
   method, ins ece, ins nll, ssc nonempty ece, ssc empty ece, ssc nonempty nll, ssc empty nll,  count, inference time
   subnet 0,  0.6447, 5.5524, 0.1090, 0.0315, 0.8212, 0.6874, 12014, 0.00
   subnet 1,  0.6582, 5.5356, 0.0870, 0.0323, 0.7848, 0.7034, 12034, 0.00
   subnet 2,  0.6499, 5.5946, 0.1191, 0.0312, 0.8515, 0.6640, 11867, 0.00
   ensemble,  0.5239, 4.5508, 0.0570, 0.0216, 0.6968, 0.4914, 8982, 0.00
   allocated 24330.26740797546
   

1. Change the kitti360_root, kitti360_label_root and kitti360_preprocess_root of the training command below to your data folders respectively.

2. The log_dir is the folder to store the training logs and checkpoints.

3. Train PaSCo with MIMO (i.e. 1 subnet) using the following command with a batchsize of 2 on 2 V100-32G GPUs (1 item per GPU):

   cd PaSCo/
   python scripts/train_kitti360.py --bs=2 --n_gpus=2 \
         --exp_prefix=pasco_single_kitti360 --lr=1e-4 \
         --kitti360_root=/gpfsdswork/dataset/KITTI-360 \
         --kitti360_label_root=/gpfsdswork/dataset/SSCBench-KITTI-360 \
         --kitti360_preprocess_root=/lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti360 \
         --log_dir=logs \
         --transformer_dropout=0.2 --n_dropout_levels=3 \
         --data_aug=True --max_angle=10.0 --translate_distance=0.2 --scale_range=0.0 \
         --enable_log=True \
         --alpha=0.0 --n_infers=1
   
   

4. Train PaSCo (2 subnets) by setting --n_infers=2 (number of subnets = 2) with batchsize of 2 on 2 A100-80G GPUs (1 items per GPU):

   cd PaSCo/
   python scripts/train_kitti360.py --bs=2 --n_gpus=2 \
         --exp_prefix=pasco_kitti360 --lr=1e-4 \
         --kitti360_root=/gpfsdswork/dataset/KITTI-360 \
         --kitti360_label_root=/gpfsdswork/dataset/SSCBench-KITTI-360 \
         --kitti360_preprocess_root=/lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti360 \
         --log_dir=logs \
         --transformer_dropout=0.2 --n_dropout_levels=3 \
         --data_aug=True --max_angle=10.0 --translate_distance=0.2 --scale_range=0.0 \
         --enable_log=True \
         --alpha=0.0 --n_infers=2
   

1. Download the pretrained checkpoint of PaSCO or PaSCO without MIMO and put it into ckpt folder or use your trained checkpoint.

   cd ckpt
   wget https://github.com/astra-vision/PaSCo/releases/download/v0.1.0/pasco_single_kitti360.ckpt
   wget https://github.com/astra-vision/PaSCo/releases/download/v0.1.0/pasco_kitti360.ckpt
   

2. Evaluate PaSCo without MIMO on 1 V100-32G GPUs (1 item per GPU). ckpt/pasco_single_kitti360.ckpt is the path to the downloaded checkpoint:

   python scripts/eval_kitti360.py --n_infers=1 --model_path=ckpt/pasco_single_kitti360.ckpt \
         --kitti360_preprocess_root=/lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti360 \
         --kitti360_root=/gpfsdswork/dataset/KITTI-360 \
         --kitti360_label_root=/gpfsdswork/dataset/SSCBench-KITTI-360
   

3. Evaluate PaSCo on 1 V100-32G GPUs (1 item per GPU). ckpt/pasco_kitti360.ckpt is the path to the downloaded checkpoint:

   python scripts/eval_kitti360.py --n_infers=2 --model_path=ckpt/pasco_kitti360.ckpt \
         --kitti360_preprocess_root=/lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti360 \
         --kitti360_root=/gpfsdswork/dataset/KITTI-360 \
         --kitti360_label_root=/gpfsdswork/dataset/SSCBench-KITTI-360
   

4. Output looks like following:

   • PaSCo without MIMO:

   method, P, R, IoU, mIoU, All PQ dagger, All PQ, All SQ, All RQ, Thing PQ, Thing SQ, Thing RQ, Stuff PQ, Stuff SQ, Stuff RQ
   subnet 0, 65.07, 58.86, 44.73, 21.18, 19.50, 10.09, 56.86, 15.67, 3.71, 49.16, 6.39, 13.29, 60.71, 20.31
   ensemble, 65.07, 58.86, 44.73, 21.18, 19.50, 10.09, 56.86, 15.67, 3.71, 49.16, 6.39, 13.29, 60.71, 20.31
   =====================================
   ==> pq
   method, car, bicycle, motorcycle, truck, other-vehicle, person, road, parking, sidewalk, other-ground, building, fence, vegetation, terrain, pole, traffic-sign, other-structure, other-object
   subnet 0, 13.74, 0.00, 1.51, 5.07, 1.00, 0.91, 72.51, 6.78, 37.23, 0.53, 15.95, 0.70, 5.94, 7.28, 1.46, 9.89, 0.22, 0.97
   ensemble, 13.74, 0.00, 1.51, 5.07, 1.00, 0.91, 72.51, 6.78, 37.23, 0.53, 15.95, 0.70, 5.94, 7.28, 1.46, 9.89, 0.22, 0.97
   ==> sq
   method, car, bicycle, motorcycle, truck, other-vehicle, person, road, parking, sidewalk, other-ground, building, fence, vegetation, terrain, pole, traffic-sign, other-structure, other-object
   subnet 0, 58.36, 0.00, 55.35, 56.86, 55.39, 69.01, 76.30, 60.57, 58.73, 62.33, 55.46, 57.64, 53.99, 56.83, 56.50, 65.51, 55.38, 69.22
   ensemble, 58.36, 0.00, 55.35, 56.86, 55.39, 69.01, 76.30, 60.57, 58.73, 62.33, 55.46, 57.64, 53.99, 56.83, 56.50, 65.51, 55.38, 69.22
   ==> rq
   method, car, bicycle, motorcycle, truck, other-vehicle, person, road, parking, sidewalk, other-ground, building, fence, vegetation, terrain, pole, traffic-sign, other-structure, other-object
   subnet 0, 23.55, 0.00, 2.73, 8.91, 1.80, 1.32, 95.03, 11.19, 63.39, 0.85, 28.75, 1.21, 11.00, 12.81, 2.59, 15.10, 0.41, 1.39
   ensemble, 23.55, 0.00, 2.73, 8.91, 1.80, 1.32, 95.03, 11.19, 63.39, 0.85, 28.75, 1.21, 11.00, 12.81, 2.59, 15.10, 0.41, 1.39
   [1.7900667190551758, 0.6379802227020264, 0.8541789054870605, 0.5246858596801758, 0.539797306060791, 0.570587158203125]
   inference time:  0.564805249520018
   [0.004273653030395508, 0.0037992000579833984, 0.0037772655487060547, 0.0036439895629882812, 0.0037114620208740234, 0.0037202835083007812]
   ensemble time:  0.003746812533098316
   Uncertainty threshold:  0.5
   =====================================
   method, ins ece, ins nll, ssc nonempty ece, ssc empty ece, ssc nonempty nll, ssc empty nll,  count, inference time
   subnet 0,  0.7872, 5.3554, 0.2259, 0.1306, 1.1598, 3.5766, 36668, 0.00
   ensemble,  0.7872, 5.3554, 0.2259, 0.1306, 1.1598, 3.5766, 36668, 0.00
   allocated 9058.6876443418
   

   • PaSCo:

   method, P, R, IoU, mIoU, All PQ dagger, All PQ, All SQ, All RQ, Thing PQ, Thing SQ, Thing RQ, Stuff PQ, Stuff SQ, Stuff RQ
   subnet 0, 59.27, 68.86, 46.74, 20.39, 20.78, 10.96, 55.53, 17.42, 4.47, 46.48, 7.89, 14.20, 60.05, 22.19
   subnet 1, 57.90, 69.95, 46.37, 20.14, 20.35, 10.57, 58.42, 16.77, 4.02, 56.09, 7.03, 13.84, 59.58, 21.65
   ensemble, 62.66, 65.70, 47.22, 22.07, 28.43, 11.04, 52.86, 17.34, 5.09, 47.10, 8.93, 14.02, 55.74, 21.55
   =====================================
   ==> pq
   method, car, bicycle, motorcycle, truck, other-vehicle, person, road, parking, sidewalk, other-ground, building, fence, vegetation, terrain, pole, traffic-sign, other-structure, other-object
   subnet 0, 14.98, 0.00, 1.20, 7.34, 1.53, 1.79, 70.77, 4.22, 34.66, 0.25, 24.68, 0.62, 10.31, 6.42, 3.31, 12.67, 0.32, 2.19
   subnet 1, 14.12, 0.18, 0.92, 5.76, 1.57, 1.55, 70.25, 4.13, 33.80, 0.29, 24.80, 0.73, 9.59, 6.28, 2.50, 12.09, 0.13, 1.55
   ensemble, 16.57, 0.00, 1.59, 8.89, 1.92, 1.58, 71.71, 4.08, 36.25, 0.00, 23.52, 0.61, 8.91, 4.66, 3.27, 13.23, 0.11, 1.82
   ==> sq
   method, car, bicycle, motorcycle, truck, other-vehicle, person, road, parking, sidewalk, other-ground, building, fence, vegetation, terrain, pole, traffic-sign, other-structure, other-object
   subnet 0, 58.22, 0.00, 52.01, 54.43, 54.62, 59.60, 74.87, 58.31, 57.89, 62.00, 55.82, 57.20, 54.99, 56.37, 56.55, 66.91, 54.67, 65.05
   subnet 1, 57.65, 52.41, 52.77, 56.32, 54.96, 62.41, 74.45, 58.30, 57.92, 58.69, 55.82, 60.48, 54.95, 56.47, 55.98, 67.00, 53.16, 61.78
   ensemble, 58.39, 0.00, 54.00, 55.12, 54.42, 60.70, 75.73, 57.44, 59.22, 0.00, 55.90, 63.81, 55.49, 58.33, 55.86, 67.74, 55.85, 63.47
   ==> rq
   method, car, bicycle, motorcycle, truck, other-vehicle, person, road, parking, sidewalk, other-ground, building, fence, vegetation, terrain, pole, traffic-sign, other-structure, other-object
   subnet 0, 25.72, 0.00, 2.31, 13.49, 2.81, 3.01, 94.52, 7.24, 59.87, 0.41, 44.21, 1.09, 18.74, 11.39, 5.86, 18.94, 0.59, 3.37
   subnet 1, 24.49, 0.35, 1.74, 10.23, 2.85, 2.49, 94.36, 7.08, 58.36, 0.49, 44.43, 1.20, 17.45, 11.12, 4.46, 18.04, 0.25, 2.51
   ensemble, 28.38, 0.00, 2.95, 16.13, 3.53, 2.61, 94.70, 7.11, 61.22, 0.00, 42.09, 0.96, 16.06, 7.99, 5.85, 19.53, 0.19, 2.87
   [2.1090309619903564, 1.5366406440734863, 1.534111738204956, 1.6147339344024658, 1.208867073059082, 1.52060866355896]
   inference time:  1.3322471255736077
   [0.02621603012084961, 0.02443861961364746, 0.027606964111328125, 0.02429676055908203, 0.024660348892211914, 0.0240786075592041]
   ensemble time:  0.024390966416286672
   Uncertainty threshold:  0.5
   =====================================
   method, ins ece, ins nll, ssc nonempty ece, ssc empty ece, ssc nonempty nll, ssc empty nll,  count, inference time
   subnet 0,  0.7668, 4.9833, 0.1879, 0.1252, 1.1738, 2.6776, 35104, 0.00
   subnet 1,  0.7735, 5.1097, 0.1990, 0.1225, 1.1935, 2.6386, 35466, 0.00
   ensemble,  0.5899, 3.8083, 0.1616, 0.1068, 1.1075, 2.1397, 18990, 0.00
   allocated 17902.39387990762
   

1. Install WaffleIron in a separate conda environment:

   conda create -y -n waffleiron 
   conda activate waffleiron
   pip install pyYAML==6.0 tqdm==4.63.0 scipy==1.8.0 torch==1.11.0 tensorboard==2.8.0
   cd PaSCo/WaffleIron_mod
   pip install -e ./
   

2. Run the following command to extract point features from the pretrained WaffleIron model (require 10883Mb GPU memory) pretrained on SemanticKITTI. The extracted features will be stored in the result_folder:

  ```
  cd PaSCo/WaffleIron_mod
  python extract_point_features_robo3d.py \
  --path_dataset /gpfsdswork/dataset/SemanticKITTI-C \
  --ckpt pretrained_models/WaffleIron-48-256__kitti/ckpt_last.pth \
  --config configs/WaffleIron-48-256__kitti.yaml \
  --result_folder /lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti/waffleiron_v2 \
  --phase val \
  --num_workers 3 \
  --num_votes 10 \
  --batch_size 2
  ```

1. Download the pretrained checkpoint of PaSCO or PaSCO without MIMO and put it into ckpt folder or use your trained checkpoint.

   cd ckpt
   wget https://github.com/astra-vision/PaSCo/releases/download/v0.1.0/pasco.ckpt
   wget https://github.com/astra-vision/PaSCo/releases/download/v0.1.0/pasco_single.ckpt
   

2. Evaluate PaSCo without MIMO on 1 V100-32G GPUs (1 item per GPU) on condition=beam_missing and level=heavy. ckpt/pasco_single.ckpt is the path to the downloaded checkpoint:

   python scripts/eval_robo3d.py --n_infers=1 --model_path=ckpt/pasco_single.ckpt \
         --dataset_preprocess_root=/lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti \
         --dataset_root=/gpfsdswork/dataset/SemanticKITTI \
         --condition=beam_missing --level=heavy
   

3. Evaluate PaSCo on 1 V100-32G GPUs (1 item per GPU) on condition=beam_missing and level=heavy. ckpt/pasco.ckpt is the path to the downloaded checkpoint:

   python scripts/eval_robo3d.py --n_infers=3 --model_path=ckpt/pasco.ckpt \
         --dataset_preprocess_root=/lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti \
         --dataset_root=/gpfsdswork/dataset/SemanticKITTI \
         --condition=beam_missing --level=heavy
   

1. Install Mayavi following the official instructions.
2. Run the following command to generate the prediction using the downloaded checkpoint ckpt/pasco_single.ckpt:

   cd PaSCo/
   python scripts/save_outputs_panoptic.py --model_path=ckpt/pasco_single.ckpt \
         --dataset_preprocess_root=/lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti \
         --dataset_root=/gpfsdswork/dataset/SemanticKITTI
   

3. Draw the generated output:

   cd PaSCo/
   python scripts/visualize.py
   

We thank the authors of the following repositories for making their code and models publicly available:

• https://github.com/valeoai/WaffleIron
• https://github.com/NVIDIA/MinkowskiEngine
• https://github.com/PRBonn/MaskPLS
• https://github.com/SCPNet/Codes-for-SCPNet
• https://github.com/astra-vision/LMSCNet
• https://github.com/yanx27/JS3C-Net
• https://github.com/facebookresearch/Mask2Former
• https://github.com/ENSTA-U2IS-AI/torch-uncertainty

The research was supported by the French project SIGHT (ANR-20-CE23-0016), the ERC Starting Grant SpatialSem (101076253), and the SAMBA collaborative project co-funded by BpiFrance in the Investissement d’Avenir Program. Computation was performed using HPC resources from GENCI–IDRIS (2023-AD011014102, AD011012808R2). We thank all Astra-Vision members for their valuable feedbacks, including Andrei Bursuc and Gilles Puy for excellent suggestions and Tetiana Martyniuk for her kind proofreading.