Deep Surface Reconstruction from Point Clouds with Visibility Information

Overview

Deep Surface Reconstruction from Point Clouds with Visibility Information

Paper

Data, code and pretrained models for the paper Deep Surface Reconstruction from Point Clouds with Visibility Information.

Point cloud Reconstruction Point cloud with Visibility Reconstruction

Data

ModelNet10

  • The ModelNet10 models made watertight using ManifoldPlus can be downloaded here on Zenodo.
  • The ModelNet10 scans used in our paper can be downloaded here on Zenodo. The dataset also includes training and evaluation data for ConvONet, Points2Surf, Shape As Points, POCO and DGNN.

ShapeNetv1 (13 class subset of Choy et al.)

  • The watertight ShapeNet models can be downloaded here (provided by the authors of ONet).
  • Please open an issue if you are interested in the scans used in our paper.

Synthetic Rooms Dataset

  • The watertight scenes can be downloaded here (provided by the authors of ConvONet).
  • Please open an issue if you are interested in the scans used in our paper.

Scanning Procedure

If you want to create scans of your own dataset you can use the precompiled scan executable. It should work on most Ubuntu systems.

scan -w path/to/working/directory -i meshToScan_filename --export npz

For creating the scans used in the paper the follwing settings were used:

--points 3000 --noise 0.005 --outliers 0.0

Data Loading

You can use the dataloader.py script to load visibility augmented point clouds from the produced scans.

Code and Pretrained Models

You can find our modified code and pretrained models for the surface reconstruction methods tested in our paper below. All methods support point clouds with and without visibility information.

References

If you find the code or data in this repository useful, please consider citing the following paper:

@misc{sulzer2022deep,
      title={Deep Surface Reconstruction from Point Clouds with Visibility Information}, 
      author={Raphael Sulzer and Loic Landrieu and Alexandre Boulch and Renaud Marlet and Bruno Vallet},
      year={2022},
      eprint={2202.01810},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
Owner
Raphael Sulzer
Raphael Sulzer
Low Complexity Channel estimation with Neural Network Solutions

Interpolation-ResNet Invited paper for WSA 2021, called 'Low Complexity Channel estimation with Neural Network Solutions'. Low complexity residual con

Dianxin 10 Dec 10, 2022
QI-Q RoboMaster2022 CV Algorithm

QI-Q RoboMaster2022 CV Algorithm

2 Jan 10, 2022
Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch

PyVarInf PyVarInf provides facilities to easily train your PyTorch neural network models using variational inference. Bayesian Deep Learning with Vari

342 Dec 02, 2022
Fast and Easy Infinite Neural Networks in Python

Neural Tangents ICLR 2020 Video | Paper | Quickstart | Install guide | Reference docs | Release notes Overview Neural Tangents is a high-level neural

Google 1.9k Jan 09, 2023
Automates Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning :rocket:

MLJAR Automated Machine Learning Documentation: https://supervised.mljar.com/ Source Code: https://github.com/mljar/mljar-supervised Table of Contents

MLJAR 2.4k Dec 31, 2022
One implementation of the paper "DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing".

Introduction One implementation of the paper "DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing". Users

seq-to-mind 18 Dec 11, 2022
Repo 4 basic seminar §How to make human machine readable"

WORK IN PROGRESS... Notebooks from the Seminar: Human Machine Readable WS21/22 Introduction into programming Georg Trogemann, Christian Heck, Mattis

experimental-informatics 3 May 29, 2022
Scrutinizing XAI with linear ground-truth data

This repository contains all the experiments presented in the corresponding paper: "Scrutinizing XAI using linear ground-truth data with suppressor va

braindata lab 2 Oct 04, 2022
PyTorch implementation of the TTC algorithm

Trust-the-Critics This repository is a PyTorch implementation of the TTC algorithm and the WGAN misalignment experiments presented in Trust the Critic

0 Nov 29, 2021
To prepare an image processing model to classify the type of disaster based on the image dataset

Disaster Classificiation using CNNs bunnysaini/Disaster-Classificiation Goal To prepare an image processing model to classify the type of disaster bas

Bunny Saini 1 Jan 24, 2022
PyTorch implementation for the visual prior component (i.e. perception module) of the Visually Grounded Physics Learner [Li et al., 2020].

VGPL-Visual-Prior PyTorch implementation for the visual prior component (i.e. perception module) of the Visually Grounded Physics Learner (VGPL). Give

Toru 8 Dec 29, 2022
Namish Khanna 40 Oct 11, 2022
PPO is a very popular Reinforcement Learning algorithm at present.

PPO is a very popular Reinforcement Learning algorithm at present. OpenAI takes PPO as the current baseline algorithm. We use the PPO algorithm to train a policy to give the best action in any situat

Rosefintech 11 Aug 23, 2021
A python library for face detection and features extraction based on mediapipe library

FaceAnalyzer A python library for face detection and features extraction based on mediapipe library Introduction FaceAnalyzer is a library based on me

Saifeddine ALOUI 14 Dec 30, 2022
Get a Grip! - A robotic system for remote clinical environments.

Get a Grip! Within clinical environments, sterilization is an essential procedure for disinfecting surgical and medical instruments. For our engineeri

Jay Sharma 1 Jan 05, 2022
Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

CoGAIL Table of Content Overview Installation Dataset Training Evaluation Trained Checkpoints Acknowledgement Citations License Overview This reposito

Jeremy Wang 29 Dec 24, 2022
TextBPN Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection

TextBPN Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection; Accepted by ICCV2021. Note: The complete code (including training and t

S.X.Zhang 84 Dec 13, 2022
Object Detection using YOLO from PyImageSearch

Object Detection using YOLO from PyImageSearch By applying object detection, you’ll not only be able to determine what is in an image, but also where

Mohamed NIANG 1 Feb 09, 2022
Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

Introduction This repository contains the modified caffe library and network architectures for our paper "Automated Melanoma Recognition in Dermoscopy

Lequan Yu 47 Nov 24, 2022
Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations

Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations Requirements The code is implemented in Python and requires

1 Nov 03, 2021