- 深度学习等相关论文 笔记
- object detection 相关
[TOC]
- [Soft Anchor-Point Object Detection-1911.12448](./paper/Soft Anchor-Point Object Detection-1911.12448.pdf)
- RCNN Fast-RCNN Faster-RCNN
- RCNN-2013-Rich feature hierarchies for accurate object detection and semantic segmentation Tech report (v5)
- [Fast-RCNN-2015]
- [Faster-RCNN-2015]
- Mask R-CNN 2017
- YOLO
- SSD: Single Shot MultiBox Detector 2015
FPN Feature Pyramid Networks for Object Detection 2016
Focal Loss for Dense Object Detection
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FPN Fully Convolutional Networks for Semantic Segmentation 2014
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ResNet Deep Residual Learning for Image Recognition 2015
KITTI 3D Object Detection Evaluation
- pointnet 2017 PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- pointnet2 2017 PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
- F-Pointnet - Frustum PointNets for 3D Object Detection from RGB-D Data
- Author Homepage
- paper
- [code]
- [PointRCNN](paper/PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud-1812.04244.pdf)
- code unofficial inplementation
- [SECOND: Sparsely EmbeddedConvolutional Detection](paper/SECOND: Sparsely EmbeddedConvolutional Detection.pdf)
- [PCN: Point Completion Network](paper/1808.00671-PCN: Point Completion Network.pdf)
- paper
- [Submanifold Sparse Convolutional Networks-1706.01307](paper/Submanifold Sparse ConvolutionalNetworks-1706.01307.pdf)
- [3D Semantic Segmentation withSubmanifold Sparse Convolutional Networks-1711.10275](paper/3D Semantic Segmentation withSubmanifold Sparse Convolutional Networks-1711.10275.pdf)
- code
- paper
- [OpenPlanner Open Source Integrated Planner for Autonomous Navigation in Highly Dynamic Environments](paper/Open Source Integrated Planner for Autonomous Navigation in Highly Dynamic Environments.pdf)
- Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame.pdf
- [Baidu Apollo EM Motion Planner-1807.08048.pdf](paper/Baidu Apollo EM Motion Planner-1807.08048.pdf)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](paper/1604.07446-A Survey of Motion Planning and ControlTechniques for Self-driving Urban Vehicles.pdf)
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http://www.probabilistic-robotics.org ProbabilisticRobotics 来自google x实验室创始人Sebastian Thrun的经典著作,详细介绍了基于概率的机器人感知,定位与规划控制方法
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[Multiple View Geometry in Computer Vision (Second Edition)] 深入的介绍了机器视觉中的视角变换
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the global positioning system and inertial navigation 深入浅出介绍了GPS工作原理,IMU工作原理和Kalman滤波,并提供了多种数据融合解决方案,能够让人从头读到尾的好书。
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Visual Odometry Visual_Odometry_VO_Part_I_Scaramuzza.pdf