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 Thanks for your valuable contribution to the research community :smiley:

- Recent papers (from 2017)


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


  • [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.]


编辑于 2019-05-14