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Point Cloud Registration

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하위 태스크: 1
평가표: 24
보존된 평가 기록: 190

Point Cloud Registration is a fundamental problem in 3D computer vision and photogrammetry. Given several sets of points in different coordinate systems, the aim of registration is to find the transformation that best aligns all of them into a common coordinate system. Point Cloud Registration plays a significant role in many vision applications such as 3D model reconstruction, cultural heritage management, landslide monitoring and solar energy analysis.

Source: Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/jewettaij/superpose3d

TEASER: Fast and Certifiable Point Cloud Registration

https://github.com/jiang-hb/diffusionreg

SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation

https://github.com/jimmy15923/unsup_point_coseg

SampleNet: Differentiable Point Cloud Sampling

https://github.com/julians89/PointNetLK-Tensorflow

PointNetLK: Robust & Efficient Point Cloud Registration using PointNet

https://github.com/kylevedder/SparsePointPillars

Open3D: A Modern Library for 3D Data Processing

https://github.com/liangp/mcstn-3dsot

Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences

https://github.com/liuQuan98/GCL-KPConv

Density-invariant Features for Distant Point Cloud Registration

https://github.com/lucifer443/SpineNet-Pytorch

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

https://github.com/luckyluckydadada/lucky-Open3D-ML-0.13

Open3D: A Modern Library for 3D Data Processing

https://github.com/marckhoury/CGF

Learning Compact Geometric Features

https://github.com/mergarsal/FastCertRelPose

TEASER: Fast and Certifiable Point Cloud Registration

https://github.com/overlappredator/OverlapPredator

PREDATOR: Registration of 3D Point Clouds with Low Overlap

https://github.com/paul007pl/VRCNet

Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results

https://github.com/prs-eth/overlappredator

PREDATOR: Registration of 3D Point Clouds with Low Overlap

https://github.com/pwc-1/Paper-9/tree/main/6/LIE-IQA

Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebras

https://github.com/rajahaseeb147/PCD_Registration_ReAgent

ReAgent: Point Cloud Registration using Imitation and Reinforcement Learning

https://github.com/securade/hub

A Maximum Likelihood Approach to Extract Finite Planes from 3-D Laser Scans

https://github.com/sumo-agarwal/open3d

Open3D: A Modern Library for 3D Data Processing

https://github.com/tensorflow/tpu/tree/master/models/official/detection

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

https://github.com/uncbiag/robot

Accurate Point Cloud Registration with Robust Optimal Transport

https://github.com/vinits5/PointNetLK

PointNetLK: Robust & Efficient Point Cloud Registration using PointNet

https://github.com/weitong8591/ars_magsac

Generalized Differentiable RANSAC

https://github.com/wzh99/DCP-TF

Deep Closest Point: Learning Representations for Point Cloud Registration

https://github.com/yan-roo/SpineNet-Pytorch

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

https://github.com/yikakabu/PPF-FoldNet

PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors

https://github.com/zhulf0804/predator

PREDATOR: Registration of 3D Point Clouds with Low Overlap

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.