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