专栏首页点云PCLdeep learning paper

deep learning paper

Some high-light papers are selected just for reference, most of them are associated with machine learning(deep learning) for 3D data.

From the perspective of 3D data representation:

(1) View-based:

  1. Multi-view Convolutional Neural Networks for 3D Shape Recognition-Hang Su et al. (ICCV 2015)
  2. Multi-view 3D Models from Single Images with a Convolutional Network – Tatarchenko et al. (ECCV 2016)

(2) Voxels-based

  1. Learning Semantic Deformation Flows with 3D Convolutional Networks – Yumer et al. (ECCV 2016)
  2. 3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction – Choy et al. (ECCV 2016)
  3. Multi-view Consistency as Supervisory Signal for Learning Shape and Pose Prediction – Tulsiani et al. (CVPR 2018)

(3) Octrees

  1. Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs – Tatarchenko et al. (ICCV 2017)

(4) Volumetric Primitives

  1. Learning Shape Abstractions by Assembling Volumetric Primitives – Tulsiani et al. (CVPR 2017)

(5) Pointclouds (Classification&Segmentation&Matching)

  1. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation – Qi et al. (CVPR 2017 )
  2. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space – Qi et al. (NIPS 2017)
  3. PointCNN – Li et al. (Arxiv 2018)
  4. Frustum PointNets for 3D Object Detection from RGB-D Data – Qi et al. (CVPR 2018)
  5. PU-Net: Point Cloud Upsampling Network – Yu et al. (CVPR 2018)
  6. PPFNet: Global Context Aware Local Features for Robust 3D Point Matching – Deng et al. (CVPR 2018)
  7. Dynamic Graph CNN for Learning on Point Clouds – Wang et al. (Arxiv 2018)
  8. SO-Net: Self-Organizing Network for Point Cloud Analysis-Jiaxin Li.et al. (Arxiv 2018)

(6) Pointclouds (Generative)

  1. PSGN: A Point Set Generation Network for 3D Object Reconstruction from a Single Image – Fan et al. (CVPR 2017)
  2. DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image – Kurenkov et al. (WACV 2018)
  3. Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction – Lin et al. (AAAI 2018 )
  4. Learning Representations and Generative Models for 3D Point Clouds – Achlioptas et al. (ICLR-W 2017)

(7) Mesh

  1. Neural 3D Mesh Renderer – Kato et al. (CVPR 2018)
  2. AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation – Groueix et al. (CVPR 2018 )

本文分享自微信公众号 - 点云PCL(dianyunPCL),作者:particle

原文出处及转载信息见文内详细说明,如有侵权,请联系 yunjia_community@tencent.com 删除。

原始发表时间:2018-07-10

本文参与腾讯云自媒体分享计划,欢迎正在阅读的你也加入,一起分享。

我来说两句

0 条评论
登录 后参与评论

相关文章

  • 点云深度学习的Pytorch框架

    这是3D 点云的深度学习框架,提供常见的点云分析方法的一种通用深度学习模型。它主要依赖Pytorch Geometric和Facebook Hydra。该框架能...

    点云PCL博主
  • PCL—低层次视觉—关键点检测(NARF)

    关键点检测本质上来说,并不是一个独立的部分,它往往和特征描述联系在一起,再将特征描述和识别、寻物联系在一起。关键点检测可以说是通往高层次视觉的重要基础。但本章节...

    点云PCL博主
  • 测评活动分享

    在点云PCL公众号相机测评活动的支持下,首先拿到了小觅相机,所以这篇文章将对小觅MYNTEYE-S1030-IR在ORB-SLAM2和RTAB-Map两种SLA...

    点云PCL博主
  • 一文尽览推荐系统模型演变史

    4. 整理此文的目的是给大家一个清晰的脉络,可当作一篇小小综述。从信息过载概念的提出到推荐系统的起源,从前深度学习时代的推荐系统到劲头正热的深度推荐系统,再到最...

    张小磊
  • Github项目推荐 | 最优控制、强化学习和运动规划等主题参考文献集锦

    References on Optimal Control, Reinforcement Learning and Motion Planning

    AI研习社
  • Activation

    刘笑江
  • 【python-leetcode78-子集】子集

    输入: nums = [1,2,3] 输出: [ [3], [1], [2], [1,2,3], [1,3], [2,3], [1,...

    绝命生
  • PAT (Basic Level) Practice (中文)1036 跟奥巴马一起编程 (15 分)

    美国总统奥巴马不仅呼吁所有人都学习编程,甚至以身作则编写代码,成为美国历史上首位编写计算机代码的总统。2014 年底,为庆祝“计算机科学教育周”正式启动,奥巴马...

    glm233
  • CNN中的目标多尺度处理

    1. 后面实习要解决实例分割中的目标多尺度问题(当然不只是这个问题,还有其他的),为此对CNN中这几年的多尺度处理方法进行简要总结~_~,时间紧任务重,只记录...

    小草AI
  • CNN中的目标多尺度处理策略汇总

    1. 后面实习要解决实例分割中的目标多尺度问题(当然不只是这个问题,还有其他的),为此对CNN中这几年的多尺度处理方法进行简要总结~_~,时间紧任务重,只记录...

    小草AI

扫码关注云+社区

领取腾讯云代金券