awesome-Autopilot-algorithm
some algorithm about self-driving car，mainly including perception algorithm，2D/3D object detection，Semantic segmentation and so on
https://github.com/Hardy-Uint/awesome-Autopilot-algorithm
Last synced: 4 days ago
JSON representation
-
Uncategorized
-
Uncategorized
- 通用Cruise WorldView|开源 - 开放可视化组件便于开发者进行无人驾驶数据可视化处理。
- [斯坦福\ - 包含一个简单的自动驾驶项目以及模拟器。
- [MIT\ - 通过构建自动驾驶汽车的应用主题介绍深度学习的实践。
- [优达学城\ - 教学自动驾驶团队使用的技能和技巧。 可以在[这里](https://medium.com/self-driving-cars/term-1-in-depth-on-udacitys-self-driving-car-curriculum-ffcf46af0c08#.bfgw9uxd9)找到课程大纲 .
- 优步ATG AVS|开源 - 其主要包括两个repo: [xviz](https://github.com/uber/xviz)处理数据 和 [streetscape.gl](https://github.com/uber/streetscape.gl)进行场景渲染。
- [多伦多大学\ - 自动驾驶视觉感知研究生课程。 本课程简要介绍了定位，自我运动估计，自由空间估计，视觉识别（分类，检测，分割）等主题。
- [David Silver - 优达学城\ - 来自Udacity的David Silver回顾了他在软件工程背景下从事自动驾驶汽车工作的课程。
-
-
感知
-
目标检测
- IDA-3D: Instance-Depth-Aware 3D Object Detection from Stereo Vision for Autonomous Driving（CVPR2020） - 3D)
- DSGN: Deep Stereo Geometry Network for 3D Object Detection(CVPR2020)
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
- IPOD: Intensive Point-based Object Detector for Point Cloud
- PIXOR: Real-time 3D Object Detection from Point Clouds
- Fast Point R-CNN
- Disentangling Monocular 3D Object Detection
- Stereo R-CNN based 3D Object Detection for Autonomous Driving
- Complex-YOLO: Real-time 3D Object Detection on Point Clouds
- Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud
- MLCVNet: Multi-Level Context VoteNet for 3D Object Detection（CVPR2020)
- 3DSSD: Point-based 3D Single Stage Object Detector（CVPR2020）
- Task-Aware Monocular Depth Estimation for 3D Object Detection
- M3D-RPN: Monocular 3D Region Proposal Network for Object Detection
- Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss
- Shift R-CNN: Deep Monocular 3D Object Detection with Closed-Form Geometric Constraints
- Monocular 3D Object Detection via Geometric Reasoning on Keypoints
- Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction
- GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving
- Accurate Monocular Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving
- Deconvolutional Networks for Point-Cloud Vehicle Detection and Tracking in Driving Scenarios
- Object-Centric Stereo Matching for 3D Object Detection
- Triangulation Learning Network: from Monocular to Stereo 3D Object Detection
- Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving
- FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds
- Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud
- YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud
- LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention（CVPR2020）
- PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection(CVPR2020) - RCNN)
- Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud（CVPR2020） - GNN)
- MLOD: A multi-view 3D object detection based on robust feature fusion method
- Multi-Sensor 3D Object Box Refinement for Autonomous Driving
- Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
- DepthCN: Vehicle Detection Using 3D-LIDAR and ConvNet
- Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds
- STD: Sparse-to-Dense 3D Object Detector for Point Cloud
- StarNet: Targeted Computation for Object Detection in Point Clouds
- PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
- Improving 3D Object Detection for Pedestrians with Virtual Multi-View Synthesis Orientation Estimation
- End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection（CVPR2020） - LiDAR_e2e)
- Disentangling Monocular 3D Object Detection
- Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation（CVPR2020)
- Vehicle Detection from 3D Lidar Using Fully Convolutional Network(百度早期工作)
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
- Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks
- RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving
- BirdNet: a 3D Object Detection Framework from LiDAR information
- LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDAR
- Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection
- Class-specific Anchoring Proposal for 3D Object Recognition in LIDAR and RGB Images
- MVX-Net: Multimodal VoxelNet for 3D Object Detection
- Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation
- 3D Object Detection Using Scale Invariant and Feature Reweighting Networks
- Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss
- Shift R-CNN: Deep Monocular 3D Object Detection with Closed-Form Geometric Constraints
- Object-Centric Stereo Matching for 3D Object Detection
- Stereo R-CNN based 3D Object Detection for Autonomous Driving
- BirdNet: a 3D Object Detection Framework from LiDAR information
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- MVX-Net: Multimodal VoxelNet for 3D Object Detection
- Triangulation Learning Network: from Monocular to Stereo 3D Object Detection
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
- Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks
- LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDAR
- Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud
-
目标跟踪
-
深度图补全&修复
- HMS-Net: Hierarchical Multi-scale Sparsity-invariant Network for Sparse Depth Completion
- Sparse and noisy LiDAR completion with RGB guidance and uncertainty
- 3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume Normalization
- Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion
- Confidence Propagation through CNNs for Guided Sparse Depth Regression
- Learning Guided Convolutional Network for Depth Completion
- DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance
- PLIN: A Network for Pseudo-LiDAR Point Cloud Interpolation
- 3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume Normalization
- Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion
-
-
综述
-
传感器标定融合
-
数据融合
- LiDAR and Camera Calibration using Motion Estimated by Sensor Fusion Odometry
- Automatic extrinsic calibration between a camera and a 3D Lidar using 3D point and plane correspondences
- A Novel Method for the Extrinsic Calibration of a 2D Laser Rangefinder and a Camera
- Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard
-
相机在线标定
-
传感器标定
-
Programming Languages
Categories
Sub Categories