GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → dashidhy → awesome-point-cloud-deep-learning

dashidhy / awesome-point-cloud-deep-learning

Licence: other
Paper list of deep learning on point clouds.

Labels

point-cloud3d-vision3d-segmentation3d-deep-learning3d-detectionpaper-list

Projects that are alternatives of or similar to awesome-point-cloud-deep-learning

WS3D
Official version of 'Weakly Supervised 3D object detection from Lidar Point Cloud'(ECCV2020)
Stars: ✭ 104 (+166.67%)
Mutual labels:  point-cloud, 3d-detection
Point2Mesh
Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance (ECCV2020)
Stars: ✭ 61 (+56.41%)
Mutual labels:  point-cloud, 3d-deep-learning
label-fusion
Volumetric Fusion of Multiple Semantic Labels and Masks
Stars: ✭ 18 (-53.85%)
Mutual labels:  3d-vision, 3d-segmentation
PointCutMix
our code for paper 'PointCutMix: Regularization Strategy for Point Cloud Classification'
Stars: ✭ 42 (+7.69%)
Mutual labels:  point-cloud, 3d-deep-learning
lowshot-shapebias
Learning low-shot object classification with explicit shape bias learned from point clouds
Stars: ✭ 37 (-5.13%)
Mutual labels:  point-cloud, 3d-vision
MinkLoc3D
MinkLoc3D: Point Cloud Based Large-Scale Place Recognition
Stars: ✭ 83 (+112.82%)
Mutual labels:  point-cloud, 3d-vision
DeepI2P
DeepI2P: Image-to-Point Cloud Registration via Deep Classification. CVPR 2021
Stars: ✭ 130 (+233.33%)
Mutual labels:  point-cloud, 3d-vision
MinkLocMultimodal
MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition
Stars: ✭ 65 (+66.67%)
Mutual labels:  point-cloud, 3d-vision
SimpleView
Official Code for ICML 2021 paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline"
Stars: ✭ 95 (+143.59%)
Mutual labels:  point-cloud, 3d-vision
CurveNet
Official implementation of "Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis", ICCV 2021
Stars: ✭ 94 (+141.03%)
Mutual labels:  point-cloud, 3d-vision
Openpcdet
OpenPCDet Toolbox for LiDAR-based 3D Object Detection.
Stars: ✭ 2,199 (+5538.46%)
Mutual labels:  point-cloud, 3d-detection
attMPTI
[CVPR 2021] Few-shot 3D Point Cloud Semantic Segmentation
Stars: ✭ 118 (+202.56%)
Mutual labels:  point-cloud, 3d-segmentation
3DGNN
No description or website provided.
Stars: ✭ 56 (+43.59%)
Mutual labels:  point-cloud
awesome-early-exiting
A curated list of Early Exiting papers, benchmarks, and misc.
Stars: ✭ 48 (+23.08%)
Mutual labels:  paper-list
Depth-Guided-Inpainting
Code for ECCV 2020 "DVI: Depth Guided Video Inpainting for Autonomous Driving"
Stars: ✭ 50 (+28.21%)
Mutual labels:  point-cloud
point-cloud-segmentation
TF2 implementation of PointNet for segmenting point clouds
Stars: ✭ 33 (-15.38%)
Mutual labels:  point-cloud
realsense explorer bot
Autonomous ground exploration mobile robot which has 3-DOF manipulator with Intel Realsense D435i mounted on a Tracked skid-steer drive mobile robot. The robot is capable of mapping spaces, exploration through RRT, SLAM and 3D pose estimation of objects around it. This is an custom robot with self built URDF model.The Robot uses ROS's navigation…
Stars: ✭ 61 (+56.41%)
Mutual labels:  point-cloud
geometric adv
Geometric Adversarial Attacks and Defenses on 3D Point Clouds (3DV 2021)
Stars: ✭ 20 (-48.72%)
Mutual labels:  point-cloud
e3d
Efficient 3D Deep Learning
Stars: ✭ 44 (+12.82%)
Mutual labels:  point-cloud
NeuralPull
Implementation of ICML'2021:Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces
Stars: ✭ 149 (+282.05%)
Mutual labels:  point-cloud
View All Similar Projects ➔

Awesome papers of deep learning on point clouds

This repo collects papers on point cloud deep learning. Note that the stars I give to each paper contain personal bias for my own project, but actually I do appreciate all the works that have been done in this area. For my own purpose, I can't include all the papers that have been published. A more complete paper list since 2017 is here: https://github.com/Yochengliu/awesome-point-cloud-analysis.

1. Feature extractor

  • Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models (ICCV 2017), R. Klokov et al. [pdf] ⭐ ⭐ ⭐ ⭐
  • PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (CVPR 2017), C. R. Qi et al. [pdf] [Github] ⭐ ⭐ ⭐ ⭐ ⭐
  • PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space (NeurIPS 2017), C. R. Qi et al. [pdf] [Github] ⭐ ⭐ ⭐ ⭐ ⭐
  • PointCNN: Convolution On X-Transformed Points (NeurIPS 2018) Y. Li et al, [pdf] [Github] ⭐ ⭐ ⭐
  • A-CNN: Annularly Convolutional Neural Networks on Point Clouds (CVPR 2019), A. Komarichev et al. [pdf]
    ⭐ ⭐ ⭐
  • Relation-Shape Convolutional Neural Network for Point Cloud Analysis (CVPR 2019), Y. Liu et al. [pdf]
    ⭐ ⭐ ⭐ ⭐

Other useful links

2. Detection

Only geometry as input

Grid-based methods

  • Voting for Voting in Online Point Cloud Object Detection (RSS 2015), D. Z. Wang et al. [pdf] ⭐ ⭐ ⭐
  • Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks (ICRA 2017), M. Engelcke et al. [pdf] ⭐ ⭐ ⭐
  • 3D fully convolutional network for vehicle detection in point cloud (IROS 2017) B. Li. [pdf] [Github] ⭐ ⭐ ⭐
  • VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection (CVPR 2018), Y. Zhou et al. [pdf]
    ⭐ ⭐ ⭐ ⭐ ⭐
  • PIXOR: Real-time 3D Object Detection From Point Clouds (CVPR 2018), B. Yang et al. [pdf] ⭐ ⭐ ⭐ ⭐
  • SECOND: Sparsely Embedded Convolutional Detection (Sensors 2018) Y. Yan et al. [pdf] [Github] ⭐ ⭐ ⭐
  • PointPillars: Fast Encoders for Object Detection from Point Clouds (CVPR 2019), A. Lang et al. [pdf] [GIthub]
    ⭐ ⭐ ⭐ ⭐ ⭐
  • Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud (ArXiv 2019) S. Shi et al. [pdf] [Github] ⭐ ⭐ ⭐ ⭐

Point-based methods

  • PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud (CVPR 2019), S. Shi et al. [pdf] [Github] ⭐ ⭐ ⭐ ⭐ ⭐
  • Deep Hough Voting for 3D Object Detection in Point Clouds (ICCV 2019) C. R. Qi et al. [pdf] [Github]
    ⭐ ⭐ ⭐ ⭐ ⭐

Combining point-based and grid-based methods

  • STD: Sparse-to-Dense 3D Object Detector for Point Cloud (ICCV 2019), Z. Yang et al. [pdf] ⭐ ⭐ ⭐ ⭐
  • Fast Point R-CNN (ICCV 2019), Y. Chen et al. [pdf] ⭐ ⭐ ⭐
  • PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection (Arxiv 2019) S. Shi et al. [pdf]
    ⭐ ⭐ ⭐ ⭐ ⭐

2D proposal based

  • IPOD: Intensive Point-based Object Detector for Point Cloud (ArXiv 2018) Z. Yang et al. [pdf] ⭐ ⭐ ⭐ ⭐
  • RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement ((ArXiv 2018), K. Shin et al. [pdf]
  • Frustum PointNets for 3D Object Detection from RGB-D Data (CVPR 2018), C. R. Qi et al. [pdf] [GIthub]
    ⭐ ⭐ ⭐ ⭐ ⭐
  • Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection (CVPR 2019), Z. Wang et al. [pdf] ⭐ ⭐ ⭐

Multi-view/multi-sensor/multi-task

  • Multi-View 3D Object Detection Network for Autonomous Driving (CVPR 2017), X. Chen et al. [pdf] [Github]
    ⭐ ⭐ ⭐ ⭐
  • PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation (CVPR 2018), D. Xu et al. [pdf] ⭐ ⭐ ⭐ ⭐
  • Deep Continuous Fusion for Multi-Sensor 3D Object Detection (ECCV 2018), M. Liang et al. [pdf] ⭐ ⭐ ⭐ ⭐
  • Multi-Task Multi-Sensor Fusion for 3D Object Detection (CVPR 2019), M. Liang et al. [pdf] ⭐ ⭐ ⭐ ⭐ ⭐
  • MVX-Net: Multimodal VoxelNet for 3D Object Detection (ICRA 2019), V. A. Sindagi et al. [pdf] ⭐ ⭐ ⭐ ⭐

Other useful links

3. Segmentation

  • Recurrent Slice Networks for 3D Segmentation of Point Clouds (CVPR 2018), Q. Huang et al. [pdf] [Github]
    ⭐ ⭐ ⭐ ⭐
  • SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation (CVPR 2018), W. Wang et al. [pdf] [Github] ⭐ ⭐ ⭐ ⭐
  • Associatively Segmenting Instances and Semantics in Point Clouds (CVPR 2019), X. Long et al. [pdf]
    ⭐ ⭐ ⭐ ⭐ ⭐

...(To be completed)

4. Dataset

Note that some of these datasets don't provide point cloud data, which means you need some toolboxes to convert data from mesh or RGB-D images.

Shape understanding

Indoor scenes

Autonomous driving (Lidar point cloud)

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].