Lepard: Learning Partial point cloud matching in Rigid and Deformable scenes

Related tags

Deep Learninglepard
Overview

Lepard: Learning Partial point cloud matching in Rigid and Deformable scenes [Paper]

drawing

Method overview

drawing

4DMatch Benchmark

4DMatch is a benchmark for matching and registration of partial point clouds with time-varying geometry. It is constructed using randomly selected 1761 sequences from DeformingThings4D. Below shows point cloud pairs with different overlap ratios.

drawing

Installation

We tested the code on python 3.8.10; Pytroch version '1.7.1' or '1.9.0+cu111'; GPU model GeForce RTX-2080 or Nvidia A100.

conda env create -f environment.yml
conda activate lepard
cd cpp_wrappers; sh compile_wrappers.sh; cd ..

Download data and pretrained model

Train and evaluation on 4DMatch

Download and extract the 4DMatch split to your custom folder. Then update the data_root in configs/train/4dmatch.yaml and configs/test/4dmatch.yaml

  • Evaluate pre-trained
python main.py configs/test/4dmatch.yaml

(To switch between 4DMatch and 4DLoMatch benchmark, modify the split configuration in configs/test/4dmatch.yaml)

  • Train from scratch
python main.py configs/train/4dmatch.yaml

Train and evaluation on 3DMatch

Download and extract the 3DMatch split to your custom folder. Then update the data_root in configs/train/3dmatch.yaml and configs/test/3dmatch.yaml

  • Evaluate pre-trained
python main.py configs/test/3dmatch.yaml

(To switch between 3DMatch and 3DLoMatch benchmark, modify the split configuration in configs/test/3dmatch.yaml)

  • Train from scratch
python main.py configs/train/3dmatch.yaml

Citation

If you use Lepard code or 4DMatch data please cite:

@article{lepard2021, 
    title={Lepard: Learning partial point cloud matching in rigid and deformable scenes.}, 
    author={Yang Li and Tatsuya Harada},
    journal={arXiv preprint arXiv:2111.12591},
    year={2021}
}
[CVPR 2021] Few-shot 3D Point Cloud Semantic Segmentation

Few-shot 3D Point Cloud Semantic Segmentation Created by Na Zhao from National University of Singapore Introduction This repository contains the PyTor

117 Dec 27, 2022
RoboDesk A Multi-Task Reinforcement Learning Benchmark

RoboDesk A Multi-Task Reinforcement Learning Benchmark If you find this open source release useful, please reference in your paper: @misc{kannan2021ro

Google Research 66 Oct 07, 2022
PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io

PyStan NOTE: This documentation describes a BETA release of PyStan 3. PyStan is a Python interface to Stan, a package for Bayesian inference. StanĀ® is

Stan 229 Dec 29, 2022
Pyramid Scene Parsing Network, CVPR2017.

Pyramid Scene Parsing Network by Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, details are in project page. Introduction This

Hengshuang Zhao 1.5k Jan 05, 2023
Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX.

snc4onnx Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX. https://github.com/PINTO0309/simple-onnx-processing-tools 1.

Katsuya Hyodo 8 Oct 13, 2022
Who calls the shots? Rethinking Few-Shot Learning for Audio (WASPAA 2021)

rethink-audio-fsl This repo contains the source code for the paper "Who calls the shots? Rethinking Few-Shot Learning for Audio." (WASPAA 2021) Table

Yu Wang 34 Dec 24, 2022
Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification

Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification This repository is the official implementation of [Dealing With Misspeci

0 Oct 25, 2021
Codebase for "ProtoAttend: Attention-Based Prototypical Learning."

Codebase for "ProtoAttend: Attention-Based Prototypical Learning." Authors: Sercan O. Arik and Tomas Pfister Paper: Sercan O. Arik and Tomas Pfister,

47 2 May 17, 2022
MLSpace: Hassle-free machine learning & deep learning development

MLSpace: Hassle-free machine learning & deep learning development

abhishek thakur 293 Jan 03, 2023
The code for 'Deep Residual Fourier Transformation for Single Image Deblurring'

Deep Residual Fourier Transformation for Single Image Deblurring Xintian Mao, Yiming Liu, Wei Shen, Qingli Li and Yan Wang code will be released soon

145 Dec 13, 2022
U-Time: A Fully Convolutional Network for Time Series Segmentation

U-Time & U-Sleep Official implementation of The U-Time [1] model for general-purpose time-series segmentation. The U-Sleep [2] model for resilient hig

Mathias Perslev 176 Dec 19, 2022
An unofficial PyTorch implementation of a federated learning algorithm, FedAvg.

Federated Averaging (FedAvg) in PyTorch An unofficial implementation of FederatedAveraging (or FedAvg) algorithm proposed in the paper Communication-E

Seok-Ju Hahn 123 Jan 06, 2023
EMNLP 2021: Single-dataset Experts for Multi-dataset Question-Answering

MADE (Multi-Adapter Dataset Experts) This repository contains the implementation of MADE (Multi-adapter dataset experts), which is described in the pa

Princeton Natural Language Processing 68 Jul 18, 2022
Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors

PSML paper: Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors PSML_IONE,PSML_ABNE,PSML_DEEPLINK,PSML_SNNA: numpy

13 Nov 27, 2022
This Deep Learning Model Predicts that from which disease you are suffering.

Deep-Learning-Project This Deep Learning Model Predicts that from which disease you are suffering. This Project Covers the Topics of Deep Learning Int

Jai Viral Doshi 0 Jan 20, 2022
Code for Dual Contrastive Learning for Unsupervised Image-to-Image Translation, NTIRE, CVPRW 2021.

arXiv Dual Contrastive Learning Adversarial Generative Networks (DCLGAN) We provide our PyTorch implementation of DCLGAN, which is a simple yet powerf

119 Dec 04, 2022
A Research-oriented Federated Learning Library and Benchmark Platform for Graph Neural Networks. Accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops.

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks A Research-oriented Federated Learning Library and Benchmark Platform

FedML-AI 175 Dec 01, 2022
Implements VQGAN+CLIP for image and video generation, and style transfers, based on text and image prompts. Emphasis on ease-of-use, documentation, and smooth video creation.

VQGAN-CLIP-GENERATOR Overview This is a package (with available notebook) for running VQGAN+CLIP locally, with a focus on ease of use, good documentat

Ryan Hamilton 98 Dec 30, 2022
Benchmarks for the Optimal Power Flow Problem

Power Grid Lib - Optimal Power Flow This benchmark library is curated and maintained by the IEEE PES Task Force on Benchmarks for Validation of Emergi

A Library of IEEE PES Power Grid Benchmarks 207 Dec 08, 2022
TCPNet - Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition

Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition This is an implementation of TCPNet. Introduction For video recognition task, a g

Zilin Gao 21 Dec 08, 2022