Pri3D: Can 3D Priors Help 2D Representation Learning? [ICCV 2021]

Pri3D leverages 3D priors for downstream 2D image understanding tasks: during pre-training, we incorporate view-invariant and geometric priors from color-geometry information given by RGB-D datasets, imbuing geometric priors into learned features. We show that these 3D-imbued learned features can effectively transfer to improved performance on 2D tasks such as semantic segmentation, object detection, and instance segmentation.

[ICCV 2021 Paper] [Video]

Environment

This codebase was tested with the following environment configurations.

Ubuntu 20.04
CUDA 11.2
GCC 7.3.0
Python 3.8.8
PyTorch 1.7.1
COLMAP v3.7
MinkowskiEngine v0.5.1
Detectron2 v0.5

Installation

We use conda for the installation process:

# Install virtual env and PyTorch
conda create -n sparseconv051 python=3.8
conda activate sparseconv051
conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=11.0 -c pytorch

# Complie and install MinkowskiEngine 0.5.1.
conda install mkl mkl-include -c intel
wget https://github.com/NVIDIA/MinkowskiEngine/archive/refs/tags/v0.5.1.zip
cd MinkowskiEngine-0.5.1 
python setup.py install

Next, clone the Pri3D repository and install the requirement from the root directory.

git clone https://github.com/Sekunde/Pri3D.git
cd Pri3D
pip install -r requirements.txt

Training Mask R-CNN models requires Detectron2.

[Logging] Pri3D will create runs for logging training curves and configurations in the project named pri3d in Weights & Biases. Additionally, checkpoints and a txt log file will be stored in the specified output folder. You will be asked to input username and password of Weights & Biases in the first time to run the training code.

[Optional] If you want to pre-train the view-consistent contrastive loss on MegaDepth data, you need to install COLMAP, see their installation ducomentation.

Pre-training Section

Data Pre-processing

Prepare ScanNet Pre-training Data

For pre-training view-invariant contrastive loss, pairs of ScanNet frames data can be generated by the following code (need to change the TARGET and SCANNET_DIR accordingly in the script). This piece of code first extracts pointcloud from partial frames, and then computes a filelist of overlapped partial frames for each scene.

cd pretrain/data_preprocess/scannet
./preprocess.sh

Then a combined txt file called overlap30.txt of filelists of each scene can be generated by running the following code. This overlap30.txt should be put into folder TARGET/splits.

cd pretrain/data_preprocess/scannet
python generate_list.py --target_dir TARGET

For pre-training geometric-prior, we first generate point cloud of ScanNet reconstruction. This can be done by running following code. We use SCANNET_DATA to refer where scannet data lives and SCANNET_OUT_PATH to denote the output path of processed scannet data.

# Edit path variables: SCANNET_DATA and SCANNET_OUT_PATH
cd pretrain/data_preprocess/scannet
python collect_indoor3d_data.py --input SCANNET_DATA --output SCANNET_OUT_PATH
# copy the filelists
cp -r split SCANNET_OUT_PATH

Afterwards, we further generate chunk bbox that is used for cropping chunks by running following code. TARGET points to where the previously generated pairs of ScanNet frames are located.

cd pretrain/data_preprocess/scannet/
python chunk.py --base TARGET

Prepare MegaDepth Pre-training Data

We borrow the MegaDepth data generation code from D2-Net. After installing COLMAP and downloading the MegaDepth Dataset(including SfM models), you can run the following code to pre-process data.

cd pretrain/data_preprocess/megadepth/
python undistort_reconstructions.py --colmap_path /path/to/colmap/executable --base_path /path/to/megadepth
bash preprocess_undistorted_megadepth.sh /path/to/megadepth /path/to/output/folder

We also provide visualization of the MegaDepth data.

Prepare KITTI Pre-training Data

Download KITTI Dataset and run the following code to pre-process the data. This will create an overlap.txt file indexing the pairs of frames as well as a mapping folder storing the coordinates mapping between frames located in /path/to/kitti/dataset.

cd pretrain/data_preprocess/kitti/
python kitti_process.py --input /path/to/kitti/dataset

Pre-training on Different Datasets

To pre-train on different datasets, we provide the scripts to train Pri3D with 8 GPUs (batch_size=64, 8 per GPU) on a single server under folder pretrain/pri3d/scripts. Pri3D can also be pre-trained on the server with fewer GPUs, e.g. 4 GPUs by setting train.batsh_size=32 (8 per GPUs) and optimizer.accumulate_step=2 (effective batch_size=32x2=64) to accumulate gradients. The code is competitable with facebook hydra. Our codebase enables multi-gpu training with distributed data parallel (DDP) module in pytorch.

Pre-train on ScanNet

TARGET and SCANNET_OUT_PATH refer to the pre-processed data locations that are defined in Prepare ScanNet Pre-train Data.

cd pretrain/pri3d
export DATAPATH=TARGET 
# if pre-train with geometric-contrastive loss
export POINTCLOUD_PATH=SCANNET_OUT_PATH
# Pretrain with view-invariant loss (ResNet50 backbone)
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet VIEW=True scripts/scannet.sh
# Pretrain with geometric-contrastive loss (ResNet50 backbone)
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet GEO=True scripts/scannet.sh
# Pretrain with view-invariant and geometric-contrastive loss (ResNet50 backbone)
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet VIEW=True GEO=True scripts/scannet.sh
# Pretrain with view-invariant loss (ResNet18 backbone)
LOG_DIR=/path/to/log/folder BACKBONE=Res18UNet VIEW=True scripts/scannet.sh

Pre-train on MegaDepth

cd pretrain/pri3d
export DATAPATH=/path/to/megadepth/processed/data/folder
# Pretrain with view-invariant loss (ResNet50 backbone)
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet scripts/megadepth.sh
# Pretrain with view-invariant loss (ResNet18 backbone)
LOG_DIR=/path/to/log/folder BACKBONE=Res18UNet scripts/megadepth.sh

Pre-train on KITTI

cd pretrain/pri3d
export DATAPATH=KITTI_PATH
# Pretrain with view-invariant loss (ResNet50 backbone)
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet scripts/kitti.sh
# Pretrain with view-invariant loss (ResNet18 backbone)
LOG_DIR=/path/to/log/folder BACKBONE=Res18UNet scripts/kitti.sh

Downstream Task Section

Semantic Segmentation on ScanNet

Download scannet_frames_25k and unzip to SCANNET_SEMSEG. It should have following structures.

SCANNET_SEMSEG/
    scene0000_00/
        color/
	depth/
	instance/
	label/
	pose/
	intrinsics_color.txt
	intrinsics_depth.txt
    scene0000_01/
    ...

Export path SCANNET_SEMSEG to enviromental variable $DATAPATH and run the code to train the ResUNet models.

cd downstream/semseg/unet
export DATAPATH=SCANNET_SEMSEG
export PHASE=train
# train the model with ResNet50 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=imagenet scripts/scannet.sh
# train the model with ResNet50 backbone, train from scratch 
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=scratch scripts/scannet.sh
# train the model with ResNet50 backbone, train from scratch with 20% data.
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=scratch PHASE=train20 scripts/scannet.sh
# train the model with ResNet50 backbone, initialized with specified pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=/path/to/saved/model scripts/scannet.sh
# train the model with ResNet18 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res18UNet INIT=imagenet scripts/scannet.sh

Similarly, export environmental variable and run the code to train PSPNet and DeepLabV3/V3+ models.

# train PSPNet (ResNet50 as backbones)
cd downstream/semseg/pspnet
export DATAPATH=SCANNET_SEMSEG
# train PSPNet with ResNet50 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder INIT=imagenet scripts/scannet.sh
# train PSPNet with ResNet50 backbone, initialized with specified pre-trained model
LOG_DIR=/path/to/log/folder INIT=/path/to/checkpoint scripts/scannet.sh

# train DeepLabV3 and DeepLabV3+ models (ResNet50 as backbones)
cd downstream/semseg/deeblabv3
export DATAPATH=SCANNET_SEMSEG
# train DeepLabV3 with ResNet50 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder INIT=imagenet scripts/train_scannet_deeplapv3.sh
# train DeepLabV3+ with ResNet50 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder INIT=imagenet scripts/train_scannet_deeplapv3plus.sh
# train DeepLabV3+ with ResNet50 backbone, initialized with specified pre-trained model
LOG_DIR=/path/to/log/folder INIT=/path/to/checkpoint scripts/train_scannet_deeplabv3plus.sh

Model Zoo

PSPNet and DeepLabV3/V3+ use checkpoints in torchvision format, thus we provide the code for converting from our Pri3D pre-trained checkpoint to torchvision checkpoint.

cd downstream/conversion
python pri3d_to_torchvision.py /path/to/pre-trained/pri3d/checkpoint /path/to/output/checkpoint/in/torchvison/format

The provided pre-trained models for PSPNet and DeepLabV3/V3+ are already converted to torchvision format.

Training Data	mIoU (val)	Backbone	Pre-trained Model (on ScanNet)	Curves	Logs
100% scenes	61.7	ResNet50	Pri3D (View + Geo)	link	link
100% scenes	55.7	ResNet18	Pri3D (View + Geo)	link	link
100% scenes	62.8	PSPNet	Pri3D (View + Geo)	link	link
100% scenes	61.3	DeepLabV3	Pri3D (View + Geo)	link	link
100% scenes	61.6	DeepLabV3+	Pri3D (View + Geo)	link	link
80% scenes	60.3	ResNet50	Pri3D (View + Geo)	link	link
60% scenes	58.9	ResNet50	Pri3D (View + Geo)	link	link
40% scenes	56.2	ResNet50	Pri3D (View + Geo)	link	link
20% scenes	51.5	ResNet50	Pri3D (View + Geo)	link	link

Semantic Segmentation on KITTI

Download and unzip label for semantic and instance segmentation. unzip and organize the data folder as following structures.

KITTI_SESEG/
    image_2/
    instance/
    semantic/
    semantic_rgb/

Use following code snippets to train semantic segmentation models on KITTI data.

cd downstream/semseg
export DATAPATH=KITTI_SEMSEG
# train the model with ResNet50 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=imagenet scripts/kitti.sh
# train the model with ResNet50 backbone, train from scratch 
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=scratch scripts/kitti.sh
# train the model with ResNet50 backbone, initialized with specified pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=/path/to/saved/model scripts/kitti.sh
# train the model with ResNet18 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res18UNet INIT=imagenet scripts/kitti.sh

Model Zoo

Training Data	mIoU (val)	Backbone	Pre-trained Model	Curves	Logs
100% scenes	33.2	ResNet50	Pri3D (View) on KITTI	link	link

Semantic Segmentation on NYUv2

Download NYUv2 Dataset and unzip it to path NYUv2_SEMSEG.

cd downstream/semseg
export DATAPATH=NYUv2_SEMSEG
# train the model with ResNet50 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=imagenet scripts/nyuv2.sh
# train the model with ResNet50 backbone, train from scratch 
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=scratch scripts/nyuv2.sh
# train the model with ResNet50 backbone, initialized with specified pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=/path/to/saved/model scripts/nyuv2.sh
# train the model with ResNet18 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res18UNet INIT=imagenet scripts/nyuv2.sh

Model Zoo

Training Data	mIoU (val)	Backbone	Pre-trained Model (on ScanNet)	Curves	Logs
100% scenes	54.7	ResNet50	Pri3D (View + Geo)	link	link
100% scenes	47.6	ResNet50	MoCoV2-supIN->SN	link	link

Semantic Segmentation on Cityscapes

Download gtFine_trainvaltest and leftImg8bit_trainvaltest. Unzip and organize as following data structures.

CityScapes_SEMSEG/
    gtFine/
    leftImg8bit/

Export the data path (CityScapes_SEMSEG) to $DATAPATH environmental variable and train the models.

cd downstream/semseg
export DATAPATH=Cityscapes_SEMSEG
# train the model with ResNet50 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=imagenet scripts/cityscapes.sh
# train the model with ResNet50 backbone, train from scratch 
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=scratch scripts/cityscapes.sh
# train the model with ResNet50 backbone, initialized with specified pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res50UNet INIT=/path/to/saved/model scripts/cityscapes.sh
# train the model with ResNet18 backbone, initialized with ImageNet pre-trained model
LOG_DIR=/path/to/log/folder BACKBONE=Res18UNet INIT=imagenet scripts/cityscapes.sh

Model Zoo

Training Data	mIoU (val)	Backbone	Pre-trained Model	Curves	Logs
100% scenes	56.3	ResNet50	Pri3D (View) on KITTI	link	link
100% scenes	55.2	ResNet50	Pri3D (View) on MegaDepth	link	link

Instance Segmentation/Detection on ScanNet

For training an instance segmentation/detection model, a COCO format json file of annotation needs to be generated. We provide code to convert the ScanNet Annotation into COCO format (json file). Path SCANNET_SEMSEG refers to the location of ScanNet semantic segmentation data.

cd downstream/insseg/dataset
# generate json file of annotations for training
python scanet2coco.py --scannet_path SCANNET_SEMSEG --phase train
# generate json file of annotations for validation
python scanet2coco.py --scannet_path SCANNET_SEMSEG --phase val
# generate json file of annotations for training on 20%,40%,60%,80% data.
python scanet2coco.py --scannet_path SCANNET_SEMSEG --phase train20
python scanet2coco.py --scannet_path SCANNET_SEMSEG --phase train40
python scanet2coco.py --scannet_path SCANNET_SEMSEG --phase train60
python scanet2coco.py --scannet_path SCANNET_SEMSEG --phase train80

The code above generates json files, such as scannet_train.coco.json and scannet_val.coco.json. After having json files, the following code will train Mask R-CNN models.

cd downstream/insseg
export JSON_PATH=/path/to/json/file
export IMAGE_PATH=SCANNET_SEMSEG
# train the model with ImageNet pre-trained model 
LOG_DIR=/path/to/log/folder INIT=imagenet sbatch script/train_scannet.sh
# train the model with pre-trained model (remove 'sbatch' if training on a local machine)
LOG_DIR=/path/to/log/folder INIT=/path/to/model sbatch script/train_scannet.sh
# train the model on ScanNet 20% data
JSON_PATH=/path/to/scannet_train20.coco.json LOG_DIR=/path/to/log/folder INIT=imagenet script/train_scannet.sh

Model Zoo

Detectron2 requires a specific checkpoint format, thus we provide the code for converting from our Pri3D pre-trained checkpoint to the required checkpoint format.

cd downstream/conversion
python pri3d_to_torchvision.py /path/to/pri3d/format/checkpoint /path/to//torchvison/format/checkpoint
ptthon torchvision_to_detectron.py /path/to//torchvison/format/checkpoint /path/to/detectron/format/checkpoint

The provided pre-trained models in the following are already converted to detectron2 checkpoints.

Data	[email protected] (bbox)	[email protected] (segm)	Backbone	Pre-trained Model (on ScanNet)	Curves	Logs
100%	44.5	35.8	ResNet50	Pri3D (View + Geo)	link	link
100%	43.5	33.9	ResNet50	MoCoV2-supIN->SN	link	link

Instance Segmentation/Detection on NYUv2

Similarly to ScanNet, we provide code to convert the NYUv2 Annotation into COCO format (json files). Path NYUv2_SEMSEG refers to the location of NYUv2 semantic segmentation data.

cd downstream/insseg/dataset
# generate json file of annotations for training
python nyu2coco.py --nyu_path NYUv2_SEMSEG --phase train
# generate json file of annotations for validation
python nyu2coco.py --scannet_path NYUv2_SEMSEG --phase val

The code above generates json files, such as nyu_train.coco.json and nyu_val.coco.json. After having json files, the following code will train Mask R-CNN models.

cd downstream/insseg
export JSON_PATH=/path/to/json/file
export IMAGE_PATH=NYUv2_SEMSEG
# train the model with ImageNet pre-trained model 
LOG_DIR=/path/to/log/folder INIT=imagenet sbatch script/train_nyu.sh
# train the model with pre-trained model (remove 'sbatch' if training on a local machine)
LOG_DIR=/path/to/log/folder INIT=/path/to/model sbatch script/train_nyu.sh

Model Zoo

The provided pre-trained models in the following are already converted to Detectron2 checkpoints (convert to detectrion2 shows how to convert from Pri3D checkpoint to Detectron2 format).

Data	[email protected] (bbox)	[email protected] (segm)	Backbone	Pre-trained Model (on ScanNet)	Curves	Logs
100%	34.0	29.5	ResNet50	Pri3D (View + Geo)	link	link
100%	31.1	27.2	ResNet50	MoCoV2-supIN->SN	link	link

Instance Segmentation/Detection on COCO

Download 2017 Train Images, 2017 Val Images and 2017 Train/Val Annotations. Unzip and organize them as following structures.

$DETECTRON2_DATASETS/
    coco/
        annotations/
	    instances_{train,val}2017.json
        {train,val}2017/

Then using the following code to train instance segmentation/detection models.

cd downstream/insseg
export DETECTRON2_DATASETS=/path/to/datasets
# train the model with ImageNet pre-trained model 
LOG_DIR=/path/to/log/folder INIT=imagenet sbatch script/train_coco.sh
# train the model with pre-trained model (remove 'sbatch' if training on a local machine)
LOG_DIR=/path/to/log/folder INIT=/path/to/model sbatch script/train_coco.sh

Model Zoo

The provided pre-trained models in the following are already converted to Detectron2 checkpoints (convert to detectron2 shows how to convert from Pri3D checkpoint to Detectron2 format).

Data	[email protected] (bbox)	[email protected] (segm)	Backbone	Pre-trained Model (on ScanNet)	Curves	Logs
100%	60.6	57.5	ResNet50	Pri3D (View)	link	link

Citing our paper

@article{hou2021pri3d,
  title={Pri3D: Can 3D Priors Help 2D Representation Learning?},
  author={Hou, Ji and Xie, Saining and Graham, Benjamin and Dai, Angela and Nie{\ss}ner, Matthias},
  journal={arXiv preprint arXiv:2104.11225},
  year={2021}
}

License

Pri3D is relased under the MIT License. See the LICENSE file for more details.

[ICCV'21] Pri3D: Can 3D Priors Help 2D Representation Learning?

Related tags

Overview

Pri3D: Can 3D Priors Help 2D Representation Learning? [ICCV 2021]

Environment

Installation

Pre-training Section

Data Pre-processing

Prepare ScanNet Pre-training Data

Prepare MegaDepth Pre-training Data

Prepare KITTI Pre-training Data

Pre-training on Different Datasets

Pre-train on ScanNet

Pre-train on MegaDepth

Pre-train on KITTI

Downstream Task Section

Semantic Segmentation on ScanNet

Model Zoo

Semantic Segmentation on KITTI

Model Zoo

Semantic Segmentation on NYUv2

Model Zoo

Semantic Segmentation on Cityscapes

Model Zoo

Instance Segmentation/Detection on ScanNet

Model Zoo

Instance Segmentation/Detection on NYUv2

Model Zoo

Instance Segmentation/Detection on COCO

Model Zoo

Citing our paper

License

Owner

Ji Hou

Tool for working with Y-chromosome data from YFull and FTDNA

NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"

One Million Scenes for Autonomous Driving

Tutoriais publicados nas nossas redes sociais para obtenção de dados, análises simples e outras tarefas relevantes no mercado financeiro.

Scripts and a shader to get you started on setting up an exported Koikatsu character in Blender.

Sequence-to-Sequence learning using PyTorch

RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering

This is the offical website for paper ''Category-consistent deep network learning for accurate vehicle logo recognition''

MEDS: Enhancing Memory Error Detection for Large-Scale Applications

Database Reasoning Over Text project for ACL paper

McGill Physics Hackathon 2021: Reaction-Diffusion Models for the Generation of Biological Patterns

Implementation of Hourglass Transformer, in Pytorch, from Google and OpenAI

A collection of resources on GAN Inversion.

ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs

A playable implementation of Fully Convolutional Networks with Keras.

[ICRA2021] Reconstructing Interactive 3D Scene by Panoptic Mapping and CAD Model Alignment

PyTorch implementation for the paper Visual Representation Learning with Self-Supervised Attention for Low-Label High-Data Regime

Face uncertainty quantification or estimation using PyTorch.

E2VID_ROS - E2VID_ROS: E2VID to a real-time system

Developed an optimized algorithm which finds the most optimal path between 2 points in a 3D Maze using various AI search techniques like BFS, DFS, UCS, Greedy BFS and A*