Lidar for Autonomous Driving II (Deep Learning)

under construction ...

LiDAR for Autonomous Vehicles II(via Deep Learning)

}Online Camera LiDAR Fusion and Object Detection on Hybrid Data for Autonomous Driving

}RegNet: Multimodal Sensor Registration Using Deep Neural Networks

}Vehicle Detection from 3D Lidar Using FCN

}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

}HDNET: Exploit HD Maps for 3D Object Detection

}IPOD: Intensive Point-based Object Detector for Point Cloud

}PIXOR: Real-time 3D Object Detection from Point Clouds

}DepthCN: Vehicle Detection Using 3D-LIDAR and ConvNet

}SECOND: Sparsely Embedded Convolutional Detection

}YOLO3D: E2E RT 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud

}YOLO4D: A ST Approach for RT Multi-object Detection and Classification from LiDAR Point Clouds

}Deconvolutional Networks for Point-Cloud Vehicle Detection and Tracking in Driving Scenarios

}Fast and Furious: Real Time E2E 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net

}SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

}SEGCloud: Semantic Segmentation of 3D Point Clouds

}Multi-View 3D Object Detection Network for Autonomous Driving

}A General Pipeline for 3D Detection of Vehicles

}Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian Detection

}Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

}PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

}PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

}PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation

}Frustum PointNets for 3D Object Detection from RGB-D Data

}RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement

}Joint 3D Proposal Generation and Object Detection from View Aggregation

}SPLATNet: Sparse Lattice Networks for Point Cloud Processing

}PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud

}Deep Continuous Fusion for Multi-Sensor 3D Object Detection

}End-to-end Learning of Multi-sensor 3D Tracking by Detection