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aim-uofa avatar

aim-uofa/DyCo3D

0
View on GitHub↗
128 stars·21 forks·Python·13 views

DyCo3D

Code for the paper DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution, CVPR 2021.

Features

  • 3D Detection and Segmentation - Dynamic convolution for robust point cloud instance segmentation.

Star history

Star history chart for aim-uofa/dyco3dStar history chart for aim-uofa/dyco3d

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with DyCo3D

These projects share indexed features with DyCo3D. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • yanx27/pointnet_pointnet2_pytorchyanx27 avatar

    yanx27/Pointnet_Pointnet2_pytorch

    4,894View on GitHub↗

    This project is a PyTorch-based framework of deep learning models designed for the classification and semantic segmentation of 3D point cloud data. It provides implementations of the PointNet architecture to perform global category labeling of entire objects and detailed partitioning of large-scale 3D environments. The system handles semantic segmentation across multiple scales, ranging from identifying individual components within a single object to labeling distinct category types within large-scale scenes. The framework includes structural components for processing unordered point sets, s

    Pythonclassificationmodelnetpoint-cloud
    View on GitHub↗4,894
  • intel-isl/open3dintel-isl avatar

    intel-isl/Open3D

    13,695View on GitHub↗

    Open3D is a 3D data processing library, visualization engine, and machine learning library. It provides a framework for manipulating point clouds and meshes through specialized algorithms designed for 3D data science workflows. The project includes a toolkit for 3D scene reconstruction to generate spatial models and align surfaces from raw data. It also functions as a GPU accelerated framework that offloads intensive spatial computations to the graphics processor to increase processing speed. The library covers a broad range of capabilities including physically based light simulations for vi

    C++
    View on GitHub↗13,695
  • angeladai/3dmvangeladai avatar

    angeladai/3DMV

    213View on GitHub↗

    3DMV jointly combines RGB color and geometric information to perform 3D semantic segmentation of RGB-D scans. This work is based on our ECCV'18 paper, 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation.

    Python
    View on GitHub↗213
  • anonymous0522/raananonymous0522 avatar

    anonymous0522/RAAN

    32View on GitHub↗

    0. The CUDA and Pytorch version that is used for this work: ~~~ 'CUDA==10.0', 'torch==1.1.0', 'CUDNN==7.5.0' ~~~ Warning: We tried CUDA11.0+Torch1.7.1 on RTX3090, the AP performance is significantly lower than the aforementioned environment setup.

    Python
    View on GitHub↗32
Compare all 30 related projects→

Frequently asked questions

What does aim-uofa/dyco3d do?

Code for the paper DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution, CVPR 2021.

What are the main features of aim-uofa/dyco3d?

The main features of aim-uofa/dyco3d are: 3D Detection and Segmentation.

Which projects share features with aim-uofa/dyco3d?

Projects with overlapping indexed features include: yanx27/pointnet_pointnet2_pytorch — This project is a PyTorch-based framework of deep learning models designed for the classification and semantic… intel-isl/open3d — Open3D is a 3D data processing library, visualization engine, and machine learning library. It provides a framework… angeladai/3dmv — 3DMV jointly combines RGB color and geometric information to perform 3D semantic segmentation of RGB-D scans. This… autonomousvision/neat — This repository is for the ICCV 2021 paper NEAT: Neural Attention Fields for End-to-End Autonomous Driving. autovision-cloud/sa-det3d — By Prarthana Bhattacharyya, Chengjie Huang and Krzysztof Czarnecki. anonymous0522/raan — 0. The CUDA and Pytorch version that is used for this work: ~~~ 'CUDA==10.0', 'torch==1.1.0', 'CUDNN==7.5.0' ~~~…