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