3D目标检测论文方法汇总 【2021部分持续更新中~】

31
五月
2021

Automanous-3D-detection-methods

版权注释

该项目地址为:https://github.com/LittleYuanzi/awesome-Automanous-3D-detection-methods
2017~2020汇总部分由CSDN博主Little_sky_jty博主倾力攥写,2021部分我将对其进行维护更新为我个人所用,无任何商业目的,如有侵权,告知删除

前言

该项目主要在对近期(17年)开始的自动驾驶场景的目标检测方法做一个汇总,持续更新,也欢迎大家参与进来。为了方便表示,该项目仅仅针对自动驾驶场景,分类方法按照输入进行划分,特别地,我们也对论文实验对应的论文做出一定的标注。

在这里插入图片描述

keywords

inputs

按照传感器的输入: monocular: 单目   stereo: 双目  lidar: 点云  
如果是多种传感器融合: image+lidar: 图像+点云  

对应实验数据集

用于标注该文章实验对应的数据集: kitti: KITTI   nuse.: NuScence   waymo: Waymo   ATG4D: ATG4D   [lyft]: lyft 

代码

标注代码实现框架: Tensorflow.: TensorFlow   PyTorch.: PyTorch   PyTorch.: PyTorch  

2017

  • [CVPR] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. [tensorflow][pytorch] [lidar] 🔥⭐️
  • [CVPR] Multi-View 3D Object Detection Network for Autonomous Driving. [tensorflow] [image+lidar] [kitti]🔥 ⭐️
  • [ICRA] Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks. [code_matlab] [lidar] [kitti]⭐️
  • [IROS] 3D fully convolutional network for vehicle detection in point cloud. [tensorflow] [lidar] [kitti]🔥 ⭐️

2018

  • [CVPR] PIXOR: Real-time 3D Object Detection from Point Clouds. [pytorch] [lidar] [kitti][ATG4D]
  • [CVPR] VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection. [tensorflow] [lidar] [kitti]🔥🔥🔥 ⭐️
  • [CVPR] PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation. [code] [image+lidar] [kitti]
  • [CVPR] Frustum PointNets for 3D Object Detection from RGB-D Data. [tensorflow] [image+lidar] [kitti] 🔥 ⭐️
  • [ECCV] Deep Continuous Fusion for Multi-Sensor 3D Object Detection. [image+lidar] [kitti] [ATG4D]
  • [ECCVW] YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud. [ monocular] [kitti]
  • [ICRA] End-to-end Learning of Multi-sensor 3D Tracking by Detection. [image+lidar] [kitti]
  • [ICRA] Robust Real-Time 3D Person Detection for Indoor and Outdoor Applications. [lidar] [kitti]
  • [ICRA] A General Pipeline for 3D Detection of Vehicles.[lidar] [kitti]
  • [IROS] Joint 3D Proposal Generation and Object Detection from View Aggregation. [lidar] [kitti]⭐️
  • [IROS] Edge and Corner Detection for Unorganized 3D Point Clouds with Application to Robotic Welding. [lidar] [kitti]
  • [SENSORS] SECOND: Sparsely Embedded Convolutional Detection. [pytorch][lidar] [kitti] 🔥🔥🔥🔥
  • [arXiv] IPOD: Intensive Point-based Object Detector for Point Cloud. [image+lidar] [kitti]
  • [arXiv] Complex-YOLO: Real-time 3D Object Detection on Point Clouds. [pytorch] [lidar] [kitti] 🔥

2019

  • [CVPR] Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. [code] [stereo][kitti]
  • [CVPR] Stereo R-CNN based 3D Object Detection for Autonomous Driving. [code] [stereo][kitti]
  • [CVPR] PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud. [pytorch] [lidar] [kitti]🔥
  • [CVPR] PointPillars: Fast Encoders for Object Detection from Point Clouds. [pytorch] [lidar] [kitti]🔥
  • [CVPR] LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving.[lidar] [kitti][ATG4D]
  • [CVPRW] Attentional PointNet for 3D-Object Detection in Point Clouds. [pytorch] [lidar] [kitti]
  • [ICCV] Fast Point R-CNN. [lidar] [kitti]
  • [ICCV] STD: Sparse-to-Dense 3D Object Detector for Point Cloud.[pytorch] [lidar] [kitti]
  • [ICCV] M3D-RPN: Monocular 3D Region Proposal Network for Object Detection.[pytorch] [monocular] [kitti]
  • [ICCVW] Range Adaptation for 3D Object Detection in LiDAR. [lidar] [kitti]
  • [ICCVW] Multi-View Reprojection Architecture for Orientation Estimation. [monocular] [kitti]
  • [NeurIPS] Point-Voxel CNN for Efficient 3D Deep Learning. [lidar] [kitti]
  • [ICMLW] LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving. [lidar]
  • [ICRA] Focal Loss in 3D Object Detection. [code] [lidar] [kitti]
  • [ICRA] SEG-VoxelNet for 3D Vehicle Detection from RGB and LiDAR Data. [lidar] [kitti]
  • [ICRA] MVX-Net: Multimodal VoxelNet for 3D Object Detection. [lidar] [kitti]
  • [AAAI] MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. [monocular] [kitti]
  • [IROS] EPN: Edge-Aware PointNet for Object Recognition from Multi-View 2.5D Point Clouds. [tensorflow] [lidar] [kitti]
  • [IROS] Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. [pytorch] [lidar+image] [kitti]
  • [IROS] Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. [lidar] [kitti]
  • [3DV] IoU Loss for 2D/3D Object Detection. [lidar] [kitti]
  • [arXiv] Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. [monocular][kitti]
  • [arXiv] FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds. [code] [lidar] [kitti]
  • [CVPRW] Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. [pytorch] [monocular][kitti]🔥
  • [CVPR] Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. [pytorch] [monocular][kitti]
  • [CVPR] GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. [monocular][kitti]
  • [CVPR] ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. [monocular][kitti]
  • [CVPR] Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. [pytorch] [stereo][kitti]
  • [CoRR] 3D Backbone Network for 3D Object Detection. [code] [lidar] [kitti]
  • [arXiv] nuScenes: A multimodal dataset for autonomous driving. [link] [dataset]
  • [arXiv] Deformable Filter Convolution for Point Cloud Reasoning.[lidar] [kitti][ATG4D]
  • [arXiv] PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement.[lidar] [kitti][ATG4D]

2020

  • [TPAMI] Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud. [pytorch][lidar] [kitti]
  • [AAAI] TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. [code] [lidar] [kitti]
  • [AAAI] PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. [lidar+image] [kitti]
  • [AAAI] ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. [code] [stereo] [kitti]
  • [AAAI] Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. [monocular] [kitti]
  • [CVPR] PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. [pytorch] [lidar] [kitti] [waymo]🔥 ⭐️ 🔥 ⭐️
  • [CVPR] Structure Aware Single-stage 3D Object Detection from Point Cloud. [pytorch] [lidar] [kitti] 🔥 ⭐️
  • [CVPR]3DSSD: Point-based 3D Single Stage Object Detector. [TensorFlow] [lidar] [kitti][nusc] 🔥 ⭐️
  • [CVPR]Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. [TensorFlow] [lidar] [kitti] 🔥 ⭐️
  • [CVPR]Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. [lidar] [kitti]
  • [CVPR]PnPNet: End-to-End Perception and Prediction with Tracking in the Loop. [lidar]
  • [CVPR] Train in Germany, Test in The USA: Making 3D Object Detectors Generalize.[code] [lidar]
  • [CVPR] PointPainting: Sequential Fusion for 3D Object Detection. [lidar+image] [kitti] [nusc]
  • [CVPR] DSGN: Deep Stereo Geometry Network for 3D Object Detection. [monocular] [kitti]
  • [CVPR] Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation.[code] [stereo] [kitti]
  • [CVPR] Learning Depth-Guided Convolutions for Monocular 3D Object Detection.[code] [monocular] [kitti]
  • [CVPR] MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. [monocular] [kitti]
  • [CVPR] LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention. [lidar_video] [nusc]
  • [CVPR] Physically Realizable Adversarial Examples for LiDAR Object Detection. [lidar]
  • [CVPR]HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. [lidar] [kitti]
  • [CVPR]Learning to Evaluate Perception Models Using Planner-Centric Metrics. [lidar]
  • [CVPR]What You See is What You Get: Exploiting Visibility for 3D Object Detection. [lidar] [nusc]
  • [CVPR]MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird’s Eye View Maps. [lidar]
  • [ECCVW] Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations.[code][lidar] [kitti]
  • [ECCV] object as hotspots.[lidar] [kitti]
  • [ECCV] EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection.[lidar+image] [kitti]
  • [ECCV] 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection.[lidar+image] [kitti]
  • [ECCV] Kinematic 3D Object Detection in Monocular Video.[code][monocular_video] [kitti]
  • [ECCV] Rethinking Pseudo-LiDAR Representation.[code][monocular] [kitti]
  • [ECCV] An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds.[lidar] [kitti]
  • [ECCV] Pillar-based Object Detection for Autonomous Driving.[lidar] [waymo]
  • [ECCV] Active Perception using Light Curtains for Autonomous Driving.[code][lidar]
  • [ECCV] Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution.[lidar]
  • [ECCV] Improving 3D Object Detection through Progressive Population Based Augmentation.[lidar] [kitti]
  • [IROS] MVLidarNet: Real-Time Multi-Class Scene Understanding for Autonomous Driving Using Multiple Views.[lidar] [nusc]
  • [ACMMM] Weakly Supervised 3D Object Detection from Point Clouds.[lidar]
  • [BMVC] RV-FuseNet: Range View based Fusion of Time-Series LiDAR Data for Joint 3D Object Detection and Motion Forecasting [lidar][nusc]
  • [Sensors] 3D-GIoU: 3D Generalized Intersection over Union for Object Detection in Point Cloud [lidar][kitti]
  • [arxiv] 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds [lidar][kitti]
  • [arxiv] Center-based 3D Object Detection and Tracking [code][lidar][nusc]
  • [arxiv] Boundary-Aware Dense Feature Indicator for Single-Stage 3D Object Detection from Point Clouds [lidar][nusc]
  • [arxiv] InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling [lidar][nusc]
  • [arxiv] Quantifying Data Augmentation for LiDAR based 3D Object Detection [lidar][kitti]
  • [arxiv] Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection [lidar][kitti][nusc]
  • [arxiv] Real-time 3D object proposal generation and classification under limited processing resources [lidar][kitti]
  • [arxiv] Safety-Aware Hardening of 3D Object Detection Neural Network Systems [lidar][kitti]
  • [arxiv] Stereo RGB and Deeper LIDAR Based Network for 3D Object Detection[stereo][kitti]
  • [arxiv] SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds [code][lidar][kitti]
  • [arxiv] SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds
    [lidar][kitti]
  • [arxiv] GhostBuster: Looking Into Shadows to Detect Ghost Objects in Autonomous Vehicle 3D Sensing [lidar][kitti]
  • [arxiv] Cross-Modality 3D Object Detection [lidar][kitti]
  • [arxiv] Towards Autonomous Driving: a Multi-Modal 360∘ Perception Proposal[lidar][kitti]

2021

  • [CVPR] Center-based 3D Object Detection and Tracking. [pytorch] [lidar] [kitti] [waymo]🔥 ⭐️ 🔥 ⭐️

Suvery

  • [TPAMI] Deep Learning for 3D Point Clouds: A Survey[lidar]
  • [ARXIV] 3D Point Cloud Processing and Learning for Autonomous Driving[lidar]

项目

  • [lidar_only] second.pytorch [kitti][nusc]
  • [lidar_only] Det3D [kitti][nusc][lyft][waymo]
  • [lidar_only] OpenPCDet[kitti][nusc][waymo]
  • [lidar_image] mmdetection3d[kitti][nusc][lyft][waymo]

Reference

自动驾驶场景3D目标检测论文合集
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