The papers in this list are about Autonomous Vehicles 3D Detection and  Semantic Segmentation especially those using point clouds and in deep learning methods.

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• FusionNet: 3D Object Classification Using Multiple Data Representations
• Vehicle Detection from 3D Lidar Using Fully Convolutional Network
• Multi-View 3D Object Detection Network 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
• VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
• Frustum PointNets for 3D Object Detection from RGB-D Data
• PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation
• Joint 3D Proposal Generation and Object Detection from View Aggregation
• Recurrent Slice Networks for 3D Segmentation of Point Clouds
• A General Pipeline for 3D Detection of Vehicles
• PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation
• RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement
• PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
• IPOD: Intensive Point-based Object Detector for Point Cloud
• PointPillars: Fast Encoders for Object Detection from Point Clouds
• Three-dimensional Backbone Network for 3D Object Detection in Traffic Scenes
• PIXOR: Real-time 3D Object Detection from Point Clouds
• Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
• Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection
• Part-A² Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud
• Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds
• STD: Sparse-to-Dense 3D Object Detector for Point Cloud
• Fast Point R-CNN
• MLOD: A multi-view 3D object detection based on robust feature fusion method
• Patch Refinement - Localized 3D Object Detection
• PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module
• SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud
• Sliding Shapes for 3D Object Detection in Depth Images
• VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition
• 3d fully convolutional network for vehicle detection in point cloud
• Voting for Voting in Online Point Cloud Object Detection
• Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
• OctNet: Learning Deep 3D Representations at High Resolutions
• Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images
• SECOND: Sparsely Embedded Convolutional Detection
• Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection
• Multi-Task Multi-Sensor Fusion for 3D Object Detection
• Deep Continuous Fusion for Multi-Sensor 3D Object Detection
• Pedestrian detection combining RGB and dense LIDAR data
• Volumetric and Multi-View CNNs for Object Classification on 3D Data
• PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
• 3D-Assisted Feature Synthesis for Novel Views of an Object
• Multi-view Convolutional Neural Networks for 3D Shape Recognition