Code release for BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images

Related tags

Deep LearningBlockGAN
Overview

BlockGAN

Code release for BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images

trans add

BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images
Thu Nguyen-Phuoc, Chrisian Richardt, Long Mai, Yong-liang Yang, Niloy Mitra

Dataset

Please contact Thu Nguyen-Phuoc for datasets.

Training

  • To run the training of BlockGAN
python main.py ./config_synthetic.json --dataset Chair --input_fname_pattern ".png" 

python main.py ./config_real.json --dataset Car --input_fname_pattern ".jpg"

Help with config.json

image_path:
			Full path to the dataset directory.
gpu:
			Index number of the GPU to use. Default: 0.
batch_size:
			Batch size. Defaults is 32.
max_epochs:
			Number of epochs to train. Defaults is 50.
epoch_step:
			Number of epochs to train before starting to decrease the learning rate. Default is 25.
z_dim:
			Dimension of the noise vector. Defaults is 90.
z_dim2:
			Dimension of the noise vector. Defaults is 30.			
d_eta:
			Learning rate of the discriminator.Default is 0.0001
g_eta:
			Learning rate of the generator.Default is 0.0001
reduce_eta:
			Reduce learning rate during training.Default is False
D_update:
			Number of updates for the Discriminator for every training step.Default is 1.
G_update:
			Number of updates for the Generator for every training step.Default is 2.
beta1:
			Beta 1 for the Adam optimiser. Default is 0.5
beta2:
			Beta 2 for the Adam optimiser. Default is 0.999
discriminator:
			Name of the discriminator to use. 
generator:
			Name of the generator to use. 
view_func:
			Name of the view sampling function to use.
skew_func:
			Name of the perspective skew function to use.
train_func:
			Name of the train function to use.
build_func:
			Name of the build function to use.
style_disc:
			Use Style discriminator. Useful for training images at 128.
sample_z:
			Distribution to sample the noise fector. Default is "uniform".
add_D_noise:
			Add noise to the input of the discriminator. Default is "false".
DStyle_lambda:
			Lambda for the style discriminator loss. Default is 1.0
ele_low:
    		        Default is 70.
ele_high:
			Default is 110.
azi_low:
			Default is 0.
azi_high:
			Default is 360.
scale_low:
			Default is 1.0
scale_high:
			Default is 1.0
x_low:
			Default is 0.
x_high:
			Default is 0.
y_low:
			Default is 0.
y_high:
			Default is 0.
z_low:
			Default is 0.
z_high:
			Default is 0.
with_translation:
			To use translation in 3D transformation. Default is "true".
with_scale:
			To use scaling in 3D transformation. Default is "true".
focal_length:
			Camera parameter. Default is 35.
sensor_size:
			Camera parameter. Default is 32.
camera_dist:
			Camera distance. Default is 11.
new_size:
			Voxel grid size. Default is 16.	
size:
			Voxel grid size. Default is 16.	
output_dir: 
			Full path to the output directory.

Citation

If you use this code for your research, please cite our paper

@inproceedings{BlockGAN2020,
  title={ BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images  },
  author={Nguyen-Phuoc, Thu and Richardt, Christian and Mai, Long and Yang, Yong-Liang and Mitra, Niloy},
  booktitle =  {Advances in Neural Information Processing Systems 33},
 month = {Nov},
 year = {2020}
}
Conformer: Local Features Coupling Global Representations for Visual Recognition

Conformer: Local Features Coupling Global Representations for Visual Recognition (arxiv) This repository is built upon DeiT and timm Usage First, inst

Zhiliang Peng 378 Jan 08, 2023
Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction

Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction. arxiv This repository contains python scripts for tr

12 Dec 12, 2022
Official Pytorch implementation of ICLR 2018 paper Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge.

Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge: Official Pytorch implementation of ICLR 2018 paper Deep Learning for Phy

emmanuel 47 Nov 06, 2022
Simple Tensorflow implementation of "Adaptive Convolutions for Structure-Aware Style Transfer" (CVPR 2021)

AdaConv — Simple TensorFlow Implementation [Paper] : Adaptive Convolutions for Structure-Aware Style Transfer (CVPR 2021) Note This repository does no

Junho Kim 26 Nov 18, 2022
Graph Regularized Residual Subspace Clustering Network for hyperspectral image clustering

Graph Regularized Residual Subspace Clustering Network for hyperspectral image clustering

Yaoming Cai 5 Jul 18, 2022
natural image generation using ConvNets

The Eyescream Project Generating Natural Images using Neural Networks. For our research summary on this work, please read the Arxiv paper: http://arxi

Meta Archive 601 Nov 23, 2022
Bayesian inference for Permuton-induced Chinese Restaurant Process (NeurIPS2021).

Permuton-induced Chinese Restaurant Process Note: Currently only the Matlab version is available, but a Python version will be available soon! This is

NTT Communication Science Laboratories 3 Dec 17, 2022
E-Ink Magic Calendar that automatically syncs to Google Calendar and runs off a battery powered Raspberry Pi Zero

MagInkCal This repo contains the code needed to drive an E-Ink Magic Calendar that uses a battery powered (PiSugar2) Raspberry Pi Zero WH to retrieve

2.8k Dec 28, 2022
LBBA-boosted WSOD

LBBA-boosted WSOD Summary Our code is based on ruotianluo/pytorch-faster-rcnn and WSCDN Sincerely thanks for your resources. Newer version of our code

Martin Dong 20 Sep 19, 2022
DTCN SMP Challenge - Sequential prediction learning framework and algorithm

DTCN This is the implementation of our paper "Sequential Prediction of Social Me

Bobby 2 Jan 24, 2022
Practical Single-Image Super-Resolution Using Look-Up Table

Practical Single-Image Super-Resolution Using Look-Up Table [Paper] Dependency Python 3.6 PyTorch glob numpy pillow tqdm tensorboardx 1. Training deep

Younghyun Jo 116 Dec 23, 2022
Code repository for the paper Computer Vision User Entity Behavior Analytics

Computer Vision User Entity Behavior Analytics Code repository for "Computer Vision User Entity Behavior Analytics" Code Description dataset.csv As di

Sameer Khanna 2 Aug 20, 2022
Adversarial Self-Defense for Cycle-Consistent GANs

Adversarial Self-Defense for Cycle-Consistent GANs This is the official implementation of the CycleGAN robust to self-adversarial attacks used in pape

Dina Bashkirova 10 Oct 10, 2022
Deep Implicit Moving Least-Squares Functions for 3D Reconstruction

DeepMLS: Deep Implicit Moving Least-Squares Functions for 3D Reconstruction This repository contains the implementation of the paper: Deep Implicit Mo

103 Dec 22, 2022
Official code for article "Expression is enough: Improving traffic signal control with advanced traffic state representation"

1 Introduction Official code for article "Expression is enough: Improving traffic signal control with advanced traffic state representation". The code s

Liang Zhang 10 Dec 10, 2022
A simple approach to emable dense segmentation with ViT.

Vision Transformer Segmentation Network This implementation of ViT in pytorch uses a super simple and straight-forward way of generating an output of

HReynaud 5 Jan 03, 2023
Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation.

Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation. It was introduced in Wright, Logan G. & Onodera, Tatsuhiro et al. (2021)1 to train Physical Neural Networ

McMahon Lab 230 Jan 05, 2023
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control

My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control

yobi byte 29 Oct 09, 2022
pytorch implementation of "Contrastive Multiview Coding", "Momentum Contrast for Unsupervised Visual Representation Learning", and "Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination"

Unofficial implementation: MoCo: Momentum Contrast for Unsupervised Visual Representation Learning (Paper) InsDis: Unsupervised Feature Learning via N

Zhiqiang Shen 16 Nov 04, 2020
CaFM-pytorch ICCV ACCEPT Introduction of dataset VSD4K

CaFM-pytorch ICCV ACCEPT Introduction of dataset VSD4K Our dataset VSD4K includes 6 popular categories: game, sport, dance, vlog, interview and city.

96 Jul 05, 2022