DeepCAD: A Deep Generative Network for Computer-Aided Design Models

Overview

DeepCAD

This repository provides source code for our paper:

DeepCAD: A Deep Generative Network for Computer-Aided Design Models

Rundi Wu, Chang Xiao, Changxi Zheng

ICCV 2021 (camera ready version coming soon)

We also release the Onshape CAD data parsing scripts here: onshape-cad-parser.

Prerequisites

  • Linux
  • NVIDIA GPU + CUDA CuDNN
  • Python 3.7, PyTorch 1.5+

Dependencies

Install python package dependencies through pip:

$ pip install -r requirements.txt

Install pythonocc (OpenCASCADE) by conda:

$ conda install -c conda-forge pythonocc-core=7.5.1

Data

Download data from here (backup) and extract them under data folder.

  • cad_json contains the original json files that we parsed from Onshape and each file describes a CAD construction sequence.
  • cad_vec contains our vectorized representation for CAD sequences, which serves for fast data loading. They can also be obtained using dataset/json2vec.py. TBA.
  • Some evaluation metrics that we use requires ground truth point clouds. Run:
    $ cd dataset
    $ python json2pc.py --only_test

The data we used are parsed from Onshape public documents with links from ABC dataset. We also release our parsing scripts here for anyone who are interested in parsing their own data.

Training

See all hyper-parameters and configurations under config folder. To train the autoencoder:

$ python train.py --exp_name newDeepCAD -g 0

For random generation, further train a latent GAN:

# encode all data to latent space
$ python test.py --exp_name newDeepCAD --mode enc --ckpt 1000 -g 0

# train latent GAN (wgan-gp)
$ python lgan.py --exp_name newDeepCAD --ae_ckpt 1000 -g 0

The trained models and experment logs will be saved in proj_log/newDeepCAD/ by default.

Testing and Evaluation

Autoencoding

After training the autoencoder, run the model to reconstruct all test data:

$ python test.py --exp_name newDeepCAD --mode rec --ckpt 1000 -g 0

The results will be saved inproj_log/newDeepCAD/results/test_1000 by default in the format of h5 (CAD sequence saved in vectorized representation).

To evaluate the results:

$ cd evaluation
# for command accuray and parameter accuracy
$ python evaluate_ae_acc.py --src ../proj_log/newDeepCAD/results/test_1000
# for chamfer distance and invalid ratio
$ python evaluate_ae_cd.py --src ../proj_log/newDeepCAD/results/test_1000 --parallel

Random Generation

After training the latent GAN, run latent GAN and the autoencoder to do random generation:

# run latent GAN to generate fake latent vectors
$ python lgan.py --exp_name newDeepCAD --ae_ckpt 1000 --ckpt 200000 --test --n_samples 9000 -g 0

# run the autoencoder to decode into final CAD sequences
$ python test.py --exp_name newDeepCAD --mode dec --ckpt 1000 --z_path proj_log/newDeepCAD/lgan_1000/results/fake_z_ckpt200000_num9000.h5 -g 0

The results will be saved inproj_log/newDeepCAD/lgan_1000/results by default.

To evaluate the results by COV, MMD and JSD:

$ cd evaluation
$ sh run_eval_gen.sh ../proj_log/newDeepCAD/lgan_1000/results/fake_z_ckpt200000_num9000_dec 1000 0

The script run_eval_gen.sh combines collect_gen_pc.py and evaluate_gen_torch.py. You can also run these two files individually with specified arguments.

Pre-trained models

Download pretrained model from here (backup) and extract it under proj_log. All testing commands shall be able to excecuted directly, by specifying --exp_name=pretrained when needed.

Visualization and Export

We provide scripts to visualize CAD models and export the results to .step files, which can be loaded by almost all modern CAD softwares.

$ cd utils
$ python show.py --src {source folder} # visualize with opencascade
$ python export2step.py --src {source folder} # export to step format

Script to create CAD modeling sequence in Onshape according to generated outputs: TBA.

Acknowledgement

We would like to thank and acknowledge referenced codes from DeepSVG, latent 3d points and PointFlow.

Cite

Please cite our work if you find it useful:

@InProceedings{wu2021deepcad,
author = {Wu, Rundi and Xiao, Chang and Zheng, Changxi},
title = {DeepCAD: A Deep Generative Network for Computer-Aided Design Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021}
}
Owner
Rundi Wu
PhD student at Columbia University
Rundi Wu
Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System

Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System This repository contains code for the paper Schultheis,

2 Oct 28, 2022
PyTorch implementations of the beta divergence loss.

Beta Divergence Loss - PyTorch Implementation This repository contains code for a PyTorch implementation of the beta divergence loss. Dependencies Thi

Billy Carson 7 Nov 09, 2022
4th place solution for the SIGIR 2021 challenge.

SIGIR-2021 (Tinkoff.AI) How to start Download train and test data: https://sigir-ecom.github.io/data-task.html Place it under sigir-2021/data/. Run py

Tinkoff.AI 4 Jul 01, 2022
Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing

Notice: Support for Python 3.6 will be dropped in v.0.2.1, please plan accordingly! Efficient and Scalable Physics-Informed Deep Learning Collocation-

tensordiffeq 74 Dec 09, 2022
Matplotlib Image labeller for classifying images

mpl-image-labeller Use Matplotlib to label images for classification. Works anywhere Matplotlib does - from the notebook to a standalone gui! For more

Ian Hunt-Isaak 5 Sep 24, 2022
Simple tools for logging and visualizing, loading and training

TNT TNT is a library providing powerful dataloading, logging and visualization utilities for Python. It is closely integrated with PyTorch and is desi

1.5k Jan 02, 2023
Adversarial examples to the new ConvNeXt architecture

Adversarial examples to the new ConvNeXt architecture To get adversarial examples to the ConvNeXt architecture, run the Colab: https://github.com/stan

Stanislav Fort 19 Sep 18, 2022
Deep learning operations reinvented (for pytorch, tensorflow, jax and others)

This video in better quality. einops Flexible and powerful tensor operations for readable and reliable code. Supports numpy, pytorch, tensorflow, and

Alex Rogozhnikov 6.2k Jan 01, 2023
Code related to the manuscript "Averting A Crisis In Simulation-Based Inference"

Abstract We present extensive empirical evidence showing that current Bayesian simulation-based inference algorithms are inadequate for the falsificat

Montefiore Artificial Intelligence Research 3 Nov 14, 2022
Source code for our Paper "Learning in High-Dimensional Feature Spaces Using ANOVA-Based Matrix-Vector Multiplication"

NFFT4ANOVA Source code for our Paper "Learning in High-Dimensional Feature Spaces Using ANOVA-Based Matrix-Vector Multiplication" This package uses th

Theresa Wagner 1 Aug 10, 2022
Official Pytorch Implementation of Relational Self-Attention: What's Missing in Attention for Video Understanding

Relational Self-Attention: What's Missing in Attention for Video Understanding This repository is the official implementation of "Relational Self-Atte

mandos 43 Dec 07, 2022
SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021]

SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021] Pdf: https://openreview.net/forum?id=v5gjXpmR8J Code for our ICLR 2021 pape

Princeton INSPIRE Research Group 113 Nov 27, 2022
Auto HMM: Automatic Discrete and Continous HMM including Model selection

Auto HMM: Automatic Discrete and Continous HMM including Model selection

Chess_champion 29 Dec 07, 2022
Toward Multimodal Image-to-Image Translation

BicycleGAN Project Page | Paper | Video Pytorch implementation for multimodal image-to-image translation. For example, given the same night image, our

Jun-Yan Zhu 1.4k Dec 22, 2022
This is an (re-)implementation of DeepLab-ResNet in TensorFlow for semantic image segmentation on the PASCAL VOC dataset.

DeepLab-ResNet-TensorFlow This is an (re-)implementation of DeepLab-ResNet in TensorFlow for semantic image segmentation on the PASCAL VOC dataset. Up

19 Jan 16, 2022
A Protein-RNA Interface Predictor Based on Semantics of Sequences

PRIP PRIP:A Protein-RNA Interface Predictor Based on Semantics of Sequences installation gensim==3.8.3 matplotlib==3.1.3 xgboost==1.3.3 prettytable==2

李优 0 Mar 25, 2022
《Single Image Reflection Removal Beyond Linearity》(CVPR 2019)

Single-Image-Reflection-Removal-Beyond-Linearity Paper Single Image Reflection Removal Beyond Linearity. Qiang Wen, Yinjie Tan, Jing Qin, Wenxi Liu, G

Qiang Wen 51 Jun 24, 2022
Image Lowpoly based on Centroid Voronoi Diagram via python-opencv and taichi

CVTLowpoly: Image Lowpoly via Centroid Voronoi Diagram Image Sharp Feature Extraction using Guide Filter's Local Linear Theory via opencv-python. The

Pupa 4 Jul 29, 2022
商品推荐系统

商品top50推荐系统 问题建模 本项目的数据集给出了15万左右的用户以及12万左右的商品, 以及对应的经过脱敏处理的用户特征和经过预处理的商品特征,旨在为用户推荐50个其可能购买的商品。 推荐系统架构方案 本项目采用传统的召回+排序的方案。

107 Dec 29, 2022
Time Dependent DFT in Tamm-Dancoff Approximation

Density Function Theory Program - kspy-tddft(tda) This is an implementation of Time-Dependent Density Functional Theory(TDDFT) using the Tamm-Dancoff

Peter Borthwick 2 Nov 17, 2022