CVPR 2024 Oral, Best Paper Award Candidate
Anh-Quan Cao1 Angela Dai2 Raoul de Charette1
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
- 3. Panoptic labels generation
- 4. Training and evaluation
- 5. Visualization
- Acknowledgment
- 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.
-
Download the source code with git
git clone https://github.com/astra-vision/PaSCo.git
-
Create conda environment:
conda create -y -n pasco python=3.9 conda activate pasco
-
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
-
Install Minkowski Engine v0.5.4
-
Install pytorch_lightning 1.9.0 with torchmetrics 1.4.0.post0
pip install --no-cache-dir pytorch_lightning==1.9.0
-
Install the additional dependencies:
cd PaSCo/ pip install -r requirements.txt
-
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
-
Install PaSCo
pip install -e ./
Please download the following data into a folder e.g. /gpfsdswork/dataset/SemanticKITTI
and unzip:
-
The Semantic Scene Completion dataset v1.1 (
SemanticKITTI voxel data (700 MB)
) from SemanticKITTI website -
The KITTI Odometry Benchmark calibration data (Download odometry data set
(calibration files, 1 MB)
). -
The 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 filepreprocess.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
- Please download the SemanticKITTI-C following the official instruction and put in a folder e.g.
/gpfsdswork/dataset/SemanticKITTI-C
. - 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
- Create a folder to store preprocess data for Semantic KITTI dataset e.g. /lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti .
- 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
Note
This command processes 4649 files on CPU took approximately 10 hours using 10 processes. The number of processes can be adjusted by modifying the n_process
parameter.
- You can download the generated panoptic labels for Semantic KITTI:
- Go to the preprocess folder for KITTI:
cd /lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti
- Download the compressed file:
wget https://github.com/astra-vision/PaSCo/releases/download/v0.0.1/kitti_instance_label_v2.tar.gz
- Extract the file:
tar xvf kitti_instance_label_v2.tar.gz
- Go to the preprocess folder for KITTI:
- 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
- Create a folder to store preprocess data for SSCBench-KITTI360 dataset e.g. /lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti360 .
- 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
- You can download the generated panoptic labels for SSCBench-KITTI360:
- Go to the preprocess folder for KITTI360:
cd /lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti360
- Create a folder to store the instance label and cd into it:
mkdir instance_labels_v2 cd instance_labels_v2
- 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
- Extract all the downloaded files:
tar xvf *.tar.gz
- Go to the preprocess folder for KITTI360:
- 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
Important
During training, the reported metric is lower than the final metrics because we limit the number of generated voxels to prevent running out of memory. The training metrics are used solely to assess the progress of the training. The final metrics are determined during evaluation.
Note
The architecture has been slightly modified from the paper to improve training stability. The paper uses 7 residual blocks after each upsampling layer and none in the corresponding encoder block. The new design uses 3 residual blocks after each upsampling layer and 3 residual blocks in the corresponding encoder block. The original design occasionally caused GPU memory errors due to the large number of generated voxels processed by 7 residual blocks. The new design is more stable and lighter, albeit with slightly lower performance.
Note
This step is only necessary when training on SemanticKITTI because of the availability of the WaffleIron pretrained model.
Tip
A better approach could be to explore the features of pretrained models available at https://github.com/valeoai/ScaLR.
- 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 ./
Caution
I used the older version of WaffleIron which requires pytorch 1.11.0.
- 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
Note
The generated instance label is supposed to be stored in os.path.join(dataset_preprocess_root, "instance_labels_v2")
-
Change the
dataset_preprocess_root
anddataset_root
of the training command below to the preprocess and raw data folder respectively. -
The
log_dir
is the folder to store the training logs and checkpoints. -
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
-
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
-
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
-
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
-
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
-
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
Important
Note that voxel ece = (ssc empty ece + ssc nonempty ece)/2 and voxel nll = (ssc empty nll + ssc nonempty nll)/2.
The inference time reported in the paper was measured on an A100 GPU, making it faster than on a V100. For SemanticKITTI, the time also includes the WaffleIron feature extraction duration.
Note
The generated instance label is supposed to be stored in os.path.join(dataset_preprocess_root, "instance_labels_v2")
.
-
Change the
kitti360_root
,kitti360_label_root
andkitti360_preprocess_root
of the training command below to your data folders respectively. -
The
log_dir
is the folder to store the training logs and checkpoints. -
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
-
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
-
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
-
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
-
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
-
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
- 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 ./
Caution
I used the older version of WaffleIron which requires pytorch 1.11.0.
- 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
:
Important
/gpfsdswork/dataset/SemanticKITTI-C
is the path to the SemanticKITTI-C dataset and /lustre/fsn1/projects/rech/kvd/uyl37fq/pasco_preprocess/kitti
is the path to store the extracted features.
```
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
```
-
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
-
Evaluate PaSCo without MIMO on 1 V100-32G GPUs (1 item per GPU) on
condition=beam_missing
andlevel=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
-
Evaluate PaSCo on 1 V100-32G GPUs (1 item per GPU) on
condition=beam_missing
andlevel=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
Note
The Robo3D dataset contains 8 conditions and 3 levels. The conditions are beam_missing
, fog
, cross_sensor
, crosstalk
, incomplete_echo
, motion_blur
, snow
, and wet_ground
. The levels are categorized as light
, moderate
, and heavy
. You can modify the condition and level parameters to evaluate the model under different settings.
- Install Mayavi following the official instructions.
- 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
- 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.