OAmbre/OcCo

OcCo: Occlusion Completion for Point Cloud Pre-Training

This repository is the official implementation of "OcCo: Occlusion Completion for Point Cloud Pre-Training"

[Paper] [Project Page] [Poster]

Intro

In this work, we train a completion model that learns how to reconstruct the occluded points, given the partial point cloud. In this way, our method learns a pre-trained representation that can identify the visual constraints inherently embedded in real-world point clouds.

We call our method Occlusion Completion (OcCo). We demonstrate that OcCo learns representations that: improve generalization on downstream tasks over prior pre-training methods, transfer to different datasets, reduce training time, and improve labeled sample efficiency.

Citation

If you find our work useful or insightful, please cite:

Usage

We provide codes in both PyTorch (1.3): OcCo_Torch and TensorFlow (1.13-1.15): OcCo_TF , both can reproduce the results reported in the paper. We also provide with docker configuration */docker. Our recommended development environment PyTorch + docker, the following description are based on OcCo_Torch, we refer the readme in the OcCo_TF for the details of TensorFlow implementation.

1) Prerequisite

Docker

In the docker folder, we provide the build, configuration and launch scripts:

docker
| - Dockerfile_Torch  # configuration
| - build_docker_torch.sh  # scripts for building up from the docker images
| - launch_docker_torch.sh  # launch from the built image
| - .dockerignore  # ignore the log and data folder while building up

which can be automated set up via following setups:

# build up from docker images
cd OcCo_Torch/docker
sh build_docker_torch.sh

# launch the docker image, conduct completion/classification/segmentation experiments
cd OcCo_Torch/docker
sh launch_docker_torch.sh

Non-Docker Setup

Just go with pip install -r Requirements_Torch.txt with the PyTorch 1.3.0, CUDA 10.1, CUDNN 7 (you may encounter errors while building the C++ extension chamfer_distance/ for calculating the Chamfer Distance), my development environment besides docker is Ubuntu 16.04.6 LTS, gcc/g++ 5.4.0, cuda10.1, CUDNN 7.

2) Pre-Training via Occlusion Completion (OcCo)

Data Usage:

For the details in the data setup, please see data/readme.md.

Training Scripts:

We unify the training of all three models (PointNet, PCN and DGCNN) in train_completion.py. We also provide the bash template for training each models, see bash_template/train_completion_template.sh for details:

#!/usr/bin/env bash

cd ../

# train pointnet-occo model on ModelNet, from scratch
python train_completion.py \
	--gpu 0,1 \
	--dataset modelnet \
	--model pointnet_occo \
	--log_dir modelnet_pointnet_vanilla ;

# train dgcnn-occo model on ShapeNet, from scratch
python train_completion.py \
	--gpu 0,1 \
	--batch_size 16 \
	--dataset shapenet \
	--model dgcnn_occo \
	--log_dir shapenet_dgcnn_vanilla ;

Pre-Trained Weights

We will provide the OcCo Pre-Trained model for all the three models, you can use them for visualization of completing self-occluded point cloud, fine tuning on classification and semantic segmentation tasks.

3) Fine Tuning - Classification

Data Usage:

For the details in the data setup, please see data/readme.md.

Training/Testing Scripts:

We unify the training and testing of all three models (PointNet, PCN and DGCNN) in train_cls.py. We also provide the bash template for training each models from scratch, JigSaw/OcCo Pre-Trained checkpoints, see bash_template/train_cls_template.sh for details:

#!/usr/bin/env bash

cd ../

# training pointnet on ModelNet40, from scratch
python train_cls.py \
	--gpu 0 \
	--model pointnet_cls \
	--dataset modelnet40 \
	--log_dir modelnet40_pointnet_scratch ;


# fine tuning pcn on ScanNet10, using jigsaw pre-trained checkpoints
python train_cls.py \
	--gpu 0 \
	--model pcn_cls \
	--dataset scannet10 \
	--log_dir scannet10_pcn_jigsaw \
	--restore \
	--restore_path log/completion/modelnet_pcn_vanilla/checkpoints/best_model.pth ;


# fine tuning dgcnn on ScanObjectNN(OBJ_BG), using jigsaw pre-trained checkpoints
python train_cls.py \
	--gpu 0,1 \
	--epoch 250 \
	--use_sgd \
	--scheduler cos \
	--model dgcnn_cls \
	--dataset scanobjectnn \
	--bn \
	--log_dir scanobjectnn_dgcnn_occo \
	--restore \
	--restore_path log/completion/modelnet_dgcnn_vanilla/checkpoints/best_model.pth ;


# test pointnet on ModelNet40 from pre-trained checkpoints
python train_cls.py \
	--gpu 1 \
	--epoch 1 \
	--mode test \
	--model pointnet_cls \
	--dataset modelnet40 \
	--log_dir modelnet40_pointnet_scratch \
	--restore \
	--restore_path log/cls/modelnet40_pointnet_scratch/checkpoints/best_model.pth ;

4) Fine Tuning - Semantic Segmentation

Data Usage:

For the details in the data setup, please see data/readme.md.

Training/Testing Scripts:

We unify the training and testing of all three models (PointNet, PCN and DGCNN) in train_semseg.py. We also provide the bash template for training each models from scratch, JigSaw/OcCo Pre-Trained checkpoints, see bash_template/train_semseg_template.sh for details:

#!/usr/bin/env bash

cd ../

# train pointnet_semseg on 6-fold cv of S3DIS, from scratch
for area in $(seq 1 1 6)
do
python train_semseg.py \
	--gpu 0,1 \
	--model pointnet_semseg \
	--bn_decay \
	--xavier_init \
	--test_area ${area} \
	--scheduler step \
	--log_dir pointnet_area${area}_scratch ;
done

# fine tune pcn_semseg on 6-fold cv of S3DIS, using jigsaw pre-trained weights
for area in $(seq 1 1 6)
do
python train_semseg.py \
	--gpu 0,1 \
	--model pcn_semseg \
	--bn_decay \
	--test_area ${area} \
	--log_dir pcn_area${area}_jigsaw \
	--restore \
	--restore_path log/jigsaw/modelnet_pcn_vanilla/checkpoints/best_model.pth ;
done

# fine tune dgcnn_semseg on 6-fold cv of S3DIS, using occo pre-trained weights
for area in $(seq 1 1 6)
do
python train_semseg.py \
	--gpu 0,1 \
	--test_area ${area} \
	--optimizer sgd \
	--scheduler cos \
	--model dgcnn_semseg \
	--log_dir dgcnn_area${area}_occo \
	--restore \
	--restore_path log/completion/modelnet_dgcnn_vanilla/checkpoints/best_model.pth ;
done

# test pointnet_semseg on 6-fold cv of S3DIS, from saved checkpoints
for area in $(seq 1 1 6)
do
python train_semseg.py \
	--gpu 0,1 \
	--mode test \
	--model pointnet_semseg \
	--test_area ${area} \
	--scheduler step \
	--log_dir pointnet_area${area}_scratch \
	--restore \
	--restore_path log/semseg/pointnet_area${area}_scratch/checkpoints/best_model.pth ;
done

Visualization:

We recommended using relevant code snippets in RandLA-Net for visualization.

5) Pre-Trained OcCo Checkpoints

6) Data Generation (Create Your Own Dataset for OcCo Pre-Training)

For the details in the self-occluded point cloud generation, please see render/readme.md`.

7) Just Completion (Complete Your Own Data with Pre-Trained Model)

We will also provide the best OcCo checkpoints we have for the completion tasks, you can use it for completing your occluded point cloud data.

8) Jigsaw Puzzle

We also provide our implementation (developed from scratch) on pre-training point cloud models via solving 3d jigsaw puzzles tasks, the method is described in this paper, while the authors did not reprocess to our code request. The details of our implementation is reported in our paper appendix.

For the details of our implementation on the data generation and training settings, please refer to supplementary of our paper and train_jigsaw.py, utils/3DPC_Data_Gen.py, bash_template/train_jigsaw_template.sh.