awesome-point-cloud-analysis

awesome-point-cloud-analysis

随着计算机时觉的发展,近年来,关于三维点云的的研究越来越多,这个趋势可以从近年来的各大顶会中略窥一二。但我们在做研究的时候也从中发现,目前关于点云的论文和数据集的介绍比较零散,因此,我的师兄刘永成带着我一起创建和维护awesome-point-cloud-analysis这个项目。

awesome-point-cloud-analysis是一个关于三维点云论文和数据集的github项目,项目的地址为


Yochengliu/awesome-point-cloud-analysisgithub.com

目前,这个项目收集了大多数自2017年以来计算机视觉各大相关顶会以及arvix上三维点云方向的论文以及目前一些目前流行的三维点云公开数据集。对于项目中的每篇论文,我们提供了其下载地址、官方代码地址,以及标注了任务类别,分为检测、分类、分割、跟踪等任务来帮助读者从中快速选择感兴趣的论文。同时,截止目前,如果论文被引量超过50或者论文源码在Github上获得超过100的star数目,我们对其都进行了相应的标注。这个项目也将作为一个长期维护的项目,旨在方便相关研究人员的研究,也欢迎大家提出宝贵的建议。

我在下面展示了2017的部分内容,由于排版系统问题,这里没有显示出论文被引量超过50或者论文源码在Github上获得超过100的star数目的标注,所以对于项目更加详细的内容,可以移步Github项目地址查看。


awesome-point-cloud-analysis


for anyone who wants to do research about 3D point cloud.

If you find the awesome paper/code/dataset or have some suggestions, please contact linhua2017@ia.ac.cn. Thanks for your valuable contribution to the research community :smiley:

- Recent papers (from 2017)

Keywords

dat.: dataset | cls.: classification | rel.: retrieval | seg.: segmentation
det.: detection | tra.: tracking | pos.: pose | dep.: depth
reg.: registration | rec.: reconstruction | aut.: autonomous driving
oth.: other, including normal-related, correspondence, mapping, matching, alignment, compression, generative model…
Statistics: :fire: code is available & stars >= 100 | :star: citation >= 50

2017

  • [CVPR] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. [tensorflow][pytorch] [cls. seg. det.] :fire: :star:
  • [CVPR] Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs. [cls.] :star:
  • [CVPR] SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation. [torch] [seg. oth.] :star:
  • [CVPR] ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. [project][git] [dat. cls. rel. seg. oth.] :fire: :star:
  • [CVPR] Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity. [oth.]
  • [CVPR] Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures. [code] [oth.]
  • [CVPR] Discriminative Optimization: Theory and Applications to Point Cloud Registration. [reg.]
  • [CVPR] 3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder. [git] [reg.]
  • [CVPR] Multi-View 3D Object Detection Network for Autonomous Driving. [tensorflow] [det. aut.] :fire: :star:
  • [ICCV] Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models. [pytorch] [cls. rel. seg.] :star:
  • [ICCV] 3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-scale 3D Point Clouds. [code] [seg.]
  • [ICCV] Colored Point Cloud Registration Revisited. [reg.]
  • [ICCV] PolyFit: Polygonal Surface Reconstruction from Point Clouds. [code] [rec.] :fire:
  • [ICCV] From Point Clouds to Mesh using Regression. [rec.]
  • [NeurIPS] PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. [tensorflow][pytorch] [cls. seg.] :fire: :star:
  • [NeurIPS] Deep Sets. [pytorch] [cls.] :star:
  • [ICRA] Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks. [code] [det. aut.] :star:
  • [ICRA] Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications. [code] [seg. aut.]
  • [ICRA] SegMatch: Segment based place recognition in 3D point clouds. [seg. oth.]
  • [ICRA] Using 2 point+normal sets for fast registration of point clouds with small overlap. [reg.]
  • [IROS] Car detection for autonomous vehicle: LIDAR and vision fusion approach through deep learning framework. [det. aut.]
  • [IROS] 3D object classification with point convolution network. [cls.]
  • [IROS] 3D fully convolutional network for vehicle detection in point cloud. [tensorflow] [det. aut.] :fire: :star:
  • [IROS] Deep learning of directional truncated signed distance function for robust 3D object recognition. [det. pos.]
  • [IROS] Analyzing the quality of matched 3D point clouds of objects. [oth.]
  • [TPAMI]Structure-aware Data Consolidation.[oth.]

2018以及往后的文章请移步项目地址~~

编辑于 2019-05-14

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