Joint parameterization and fitting of stroke clusters

Overview

StrokeStrip: Joint Parameterization and Fitting of Stroke Clusters

Dave Pagurek van Mossel1, Chenxi Liu1, Nicholas Vining1,2, Mikhail Bessmeltsev3, Alla Sheffer1

1University of British Columbia, 2NVIDIA, 3Université de Montréal

@article{strokestrip,
	title = {StrokeStrip: Joint Parameterization and Fitting of Stroke Clusters},
	author = {Pagurek van Mossel, Dave and Liu, Chenxi and Vining, Nicholas and Bessmeltsev, Mikhail and Sheffer, Alla},
	year = 2021,
	journal = {ACM Transactions on Graphics},
	publisher = {ACM},
	address = {New York, NY, USA},
	volume = 40,
	number = 4,
	doi = {10.1145/3450626.3459777}
}

StrokeStrip jointly parameterizes clusters of strokes (a) that, together, represent strips following a single intended curve (b). We compute the parameterization of this strip (c) restricted to the domain of the input strokes (d), which we then use to produce the parameterized intended curve (d).

Usage

./strokestrip input.scap [...args]

Additional optional arguments:

  • --cut: If your input strokes include sharp back-and-forth turns, this flag will use the Cornucopia library to detect and cut such strokes.
  • --debug: Generate extra SVG outputs to introspect the algorithm
  • --rainbow: Generate an SVG showing parameterized strokes coloured with a rainbow gradient (default is red-to-blue)
  • --widths: Generate fitted widths along with centerlines
  • --taper: Force fitted widths to taper to 0 at endpoints

Input format

Drawings are inputted as .scap files, which encode strokes as polylines. Strokes are contained in pairs of braces { ... }. Each stroke has a unique stroke id and a cluster id shared by all strokes that colleectively make up one intended curve. Polyline samples can omit pressure by setting it to a default value of 0.

#[width]	[height]
@[thickness]
{
	#[stroke_id]	[cluster_id]
	[x1]	[y1]	[pressure1]
	[x2]	[y2]	[pressure2]
	[x3]	[y3]	[pressure3]
	[...etc]
}
[...etc]

Example .scap inputs are found in the examples/ directory.

Stroke clusters for new .scap files can be generated using the StrokeAggregator ground truth labeling program.

Development

Dependencies

Gurobi

This package relies on the Gurobi optimization library, which must be installed and licensed on your machine. If you are at a university, a free academic license can be obtained. This project was build with Gurobi 9.0; if you are using a newer version of Gurobi, update FindGUROBI.cmake to reference your installed version (e.g. change gurobi90 to gurobi91 for version 9.1.)

Eigen 3

Ensure that Eigen is installed and that its directory is included in $CMAKE_PREFIX_PATH.

Building

StrokeStrip is configured with Cmake:

mkdir build
cd build
cmake ..
make
Owner
Dave Pagurek
Programmer and digital artist. MSc from UBC CS '21, UWaterloo Software Engineering '19.
Dave Pagurek
Defending graph neural networks against adversarial attacks (NeurIPS 2020)

GNNGuard: Defending Graph Neural Networks against Adversarial Attacks Authors: Xiang Zhang ( Zitnik Lab @ Harvard 44 Dec 07, 2022

YOLOv4-v3 Training Automation API for Linux

This repository allows you to get started with training a state-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset or label your dataset using our

BMW TechOffice MUNICH 626 Dec 31, 2022
Migration of Edge-based Distributed Federated Learning

FedFly: Towards Migration in Edge-based Distributed Federated Learning About the research Due to mobility, a device participating in Federated Learnin

qub-blesson 11 Nov 13, 2022
torchsummaryDynamic: support real FLOPs calculation of dynamic network or user-custom PyTorch ops

torchsummaryDynamic Improved tool of torchsummaryX. torchsummaryDynamic support real FLOPs calculation of dynamic network or user-custom PyTorch ops.

Bohong Chen 1 Jan 07, 2022
Reduce end to end training time from days to hours (or hours to minutes), and energy requirements/costs by an order of magnitude using coresets and data selection.

COResets and Data Subset selection Reduce end to end training time from days to hours (or hours to minutes), and energy requirements/costs by an order

decile-team 244 Jan 09, 2023
Code for the upcoming CVPR 2021 paper

The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth Jamie Watson, Oisin Mac Aodha, Victor Prisacariu, Gabriel J. Brostow and Michael

Niantic Labs 496 Dec 30, 2022
Multi-Objective Reinforced Active Learning

Multi-Objective Reinforced Active Learning Dependencies wandb tqdm pytorch = 1.7.0 numpy = 1.20.0 scipy = 1.1.0 pycolab == 1.2 Weights and Biases O

Markus Peschl 6 Nov 19, 2022
Companion repo of the UCC 2021 paper "Predictive Auto-scaling with OpenStack Monasca"

Predictive Auto-scaling with OpenStack Monasca Giacomo Lanciano*, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella 2021 IEEE/ACM 14t

Giacomo Lanciano 0 Dec 07, 2022
This repository provides the official implementation of 'Learning to ignore: rethinking attention in CNNs' accepted in BMVC 2021.

inverse_attention This repository provides the official implementation of 'Learning to ignore: rethinking attention in CNNs' accepted in BMVC 2021. Le

Firas Laakom 5 Jul 08, 2022
PyTorch code accompanying the paper "Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning" (NeurIPS 2021).

HIGL This is a PyTorch implementation for our paper: Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning (NeurIPS 2021). Our cod

Junsu Kim 20 Dec 14, 2022
Clockwork Convnets for Video Semantic Segmentation

Clockwork Convnets for Video Semantic Segmentation This is the reference implementation of arxiv:1608.03609: Clockwork Convnets for Video Semantic Seg

Evan Shelhamer 141 Nov 21, 2022
A real-time motion capture system that estimates poses and global translations using only 6 inertial measurement units

TransPose Code for our SIGGRAPH 2021 paper "TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensors". This repository

Xinyu Yi 261 Dec 31, 2022
IGCN : Image-to-graph convolutional network

IGCN : Image-to-graph convolutional network IGCN is a learning framework for 2D/3D deformable model registration and alignment, and shape reconstructi

Megumi Nakao 7 Oct 27, 2022
A library for low-memory inferencing in PyTorch.

Pylomin Pylomin (PYtorch LOw-Memory INference) is a library for low-memory inferencing in PyTorch. Installation ... Usage For example, the following c

3 Oct 26, 2022
NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions

NeoDTI NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions (Bioinformatics).

62 Nov 26, 2022
Code for "Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space"

Sparse Steerable Convolution (SS-Conv) Code for "Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and

25 Dec 21, 2022
The Official PyTorch Implementation of DiscoBox.

DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision Paper | Project page | Demo (Youtube) | Demo (Bilib

NVIDIA Research Projects 89 Jan 09, 2023
The 2nd place solution of 2021 google landmark retrieval on kaggle.

Leaderboard, taxonomy, and curated list of few-shot object detection papers.

229 Dec 13, 2022
Official implementation of the paper Do pedestrians pay attention? Eye contact detection for autonomous driving

Do pedestrians pay attention? Eye contact detection for autonomous driving Official implementation of the paper Do pedestrians pay attention? Eye cont

VITA lab at EPFL 26 Nov 02, 2022
Official implementation of "StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation" (SIGGRAPH 2021)

StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation This repository contains the official PyTorch implementation of the following

Wonjong Jang 270 Dec 30, 2022