Voxel-based Network for Shape Completion by Leveraging Edge Generation (ICCV 2021, oral)

Related tags

Deep LearningVE-PCN
Overview

Voxel-based Network for Shape Completion by Leveraging Edge Generation

This is the PyTorch implementation for the paper "Voxel-based Network for Shape Completion by Leveraging Edge Generation (ICCV 2021, oral)"

Getting Started

python version: python-3.6; cuda version: cuda-10; PyTorch version: 1.5

Compile Customized Operators

Build operators under ops by using python setup.py install.

Datasets

Our dataset PCN's dataset TopNet's dataset

Train the model

To train the models on pcn dataset: python train_edge.py
--train_pcn;
--loss_type: pcn;
--train_path: the training data;
--eval_path: the validation data;
--n_gt_points: 16384;
--n_out_points: 16384;
--density_weight:1e11;
--dense_cls_weight:1000;
--p_norm_weight:0;
--dist_regularize_weight:0;
--chamfer_weight:1e6;
--lr 0.0007.

To train the models on topnet dataset: python train_edge.py
--train_pcn;
--loss_type: topnet;
--train_path: the training data;
--eval_path: the validation data;
--n_gt_points: 2048;
--n_out_points: 2048;
--density_weight:1e10;
--dense_cls_weight:100;
--p_norm_weight:300;
--dist_regularize_weight:0.3;
--chamfer_weight:1e4;
--augment;
--lr 0.0007.

To train the models on our dataset: python train_edge.py
--train_seen;
--loss_type: topnet;
--h5_train: the training data;
--h5_val: the validation data;
--n_gt_points: 2048;
--n_out_points: 2048;
--density_weight:1e10;
--dense_cls_weight:100;
--p_norm_weight:300;
--dist_regularize_weight:0.3;
--chamfer_weight:1e4;
--lr 0.0007.

Evaluate the models

The pre-trained models can be downloaded here: Models, unzip and put them in the root directory.
To evaluate models: python test_edge.py
--loss_type: topnet or pcn;
--eval_path: the test data from different cases;
--checkpoint: the pre-trained models;
--num_gt_points: the resolution of ground truth point clouds.

Citation

@inproceedings{wang2021voxel,
     author = {Wang, Xiaogang and , Marcelo H. Ang Jr. and Lee, Gim Hee},
     title = {Voxel-based Network for Shape Completion by Leveraging Edge Generation},
     booktitle = {ICCV)},
     year = {2021},
}

Acknowledgements

Our implementations use the code from the following repository:
Chamferdistance
PointNet++
convolutional_point_cloud_decoder

We present a regularized self-labeling approach to improve the generalization and robustness properties of fine-tuning.

Overview This repository provides the implementation for the paper "Improved Regularization and Robustness for Fine-tuning in Neural Networks", which

NEU-StatsML-Research 21 Sep 08, 2022
Face Recognize System on camera AI OAK1

FRS on OAK1 Face Recognize System on camera OAK1 This project contains our work that deploy on camera OAK1 Features Anti-Spoofing Face detection Face

Tran Anh Tuan 6 Aug 08, 2022
Train DeepLab for Semantic Image Segmentation

Train DeepLab for Semantic Image Segmentation Martin Kersner, [email protected]

Martin Kersner 172 Dec 14, 2022
Charsiu: A transformer-based phonetic aligner

Charsiu: A transformer-based phonetic aligner [arXiv] Note. This is a preview version. The aligner is under active development. New functions, new lan

jzhu 166 Dec 09, 2022
We are More than Our JOints: Predicting How 3D Bodies Move

We are More than Our JOints: Predicting How 3D Bodies Move Citation This repo contains the official implementation of our paper MOJO: @inproceedings{Z

72 Oct 20, 2022
Age Progression/Regression by Conditional Adversarial Autoencoder

Age Progression/Regression by Conditional Adversarial Autoencoder (CAAE) TensorFlow implementation of the algorithm in the paper Age Progression/Regre

Zhifei Zhang 603 Dec 22, 2022
Weighted QMIX: Expanding Monotonic Value Function Factorisation

This repo contains the cleaned-up code that was used in "Weighted QMIX: Expanding Monotonic Value Function Factorisation"

whirl 82 Dec 29, 2022
EMNLP 2021 - Frustratingly Simple Pretraining Alternatives to Masked Language Modeling

Frustratingly Simple Pretraining Alternatives to Masked Language Modeling This is the official implementation for "Frustratingly Simple Pretraining Al

Atsuki Yamaguchi 31 Nov 18, 2022
Given a 2D triangle mesh, we could randomly generate cloud points that fill in the triangle mesh

generate_cloud_points Given a 2D triangle mesh, we could randomly generate cloud points that fill in the triangle mesh. Run python disp_mesh.py Or you

Peng Yu 2 Dec 24, 2021
Implementation of the method proposed in the paper "Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation"

Neural Descriptor Fields (NDF) PyTorch implementation for training continuous 3D neural fields to represent dense correspondence across objects, and u

167 Jan 06, 2023
Sound and Cost-effective Fuzzing of Stripped Binaries by Incremental and Stochastic Rewriting

StochFuzz: A New Solution for Binary-only Fuzzing StochFuzz is a (probabilistically) sound and cost-effective fuzzing technique for stripped binaries.

Zhuo Zhang 164 Dec 05, 2022
Implementation supporting the ICCV 2017 paper "GANs for Biological Image Synthesis"

GANs for Biological Image Synthesis This codes implements the ICCV-2017 paper "GANs for Biological Image Synthesis". The paper and its supplementary m

Anton Osokin 95 Nov 25, 2022
Using contrastive learning and OpenAI's CLIP to find good embeddings for images with lossy transformations

The official code for the paper "Inverse Problems Leveraging Pre-trained Contrastive Representations" (to appear in NeurIPS 2021).

Sriram Ravula 26 Dec 10, 2022
MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space

Update (20 Jan 2020): MODALS on text data is avialable MODALS MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space Table of Conte

38 Dec 15, 2022
Automatic packaging of the open-composite libs for OvGME

OvGME Packager for OpenXR – OpenComposite for DCS Note This repository is currently unsupported and needs to be migrated to the upstream OpenComposite

12 Nov 03, 2022
Generative Adversarial Text to Image Synthesis

Text To Image Synthesis This is a tensorflow implementation of synthesizing images. The images are synthesized using the GAN-CLS Algorithm from the pa

Hao 575 Jan 08, 2023
Old Photo Restoration (Official PyTorch Implementation)

Bringing Old Photo Back to Life (CVPR 2020 oral)

Microsoft 11.3k Dec 30, 2022
IA for recognising Traffic Signs using Keras [Tensorflow]

Traffic Signs Recognition ⚠️ 🚦 Fundamentals of Intelligent Systems Introduction 📄 Development of a neural network capable of recognizing nine differ

Sebastián Fernández García 2 Dec 19, 2022
Code, Data and Demo for Paper: Controllable Generation from Pre-trained Language Models via Inverse Prompting

InversePrompting Paper: Controllable Generation from Pre-trained Language Models via Inverse Prompting Code: The code is provided in the "chinese_ip"

THUDM 101 Dec 16, 2022
git《Beta R-CNN: Looking into Pedestrian Detection from Another Perspective》(NeurIPS 2020) GitHub:[fig3]

Beta R-CNN: Looking into Pedestrian Detection from Another Perspective This is the pytorch implementation of our paper "[Beta R-CNN: Looking into Pede

35 Sep 08, 2021