Here are some papers of LiDAR perception based on deep learning methods.

2015

[ICCV 2015] Multi-view Convolutional Neural Networks for 3D Shape Recognition

[CVPR 2015] 3D deep shape descriptor

[IROS 2015] VoxNet: A 3D Convolutional Neural Network for real-time object recognition

[RSAS 2015] Voting for Voting in Online Point Cloud Object Detection

2016

[ 2016] Multi-view 3d object detection network for autonomous driving

[ 2016] Vehicle detection from 3d lidar using fully convolutional network

[ 2016] 3d fully convolutional network for vehicle detection in point cloud.

2017

[CVPR 2017] Multi-View 3D Object Detection Network for Autonomous Driving

[ArXiv 2017] 3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks

[ArXiv 2017] A Review on Deep Learning Techniques Applied to Semantic Segmentation

[ArXiv 2017] 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks

[ECCV 2017] Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models

[3DV 2017] 3D Object Classification via Spherical Projections

[CVPR 2017] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

[CG 2017] SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks

[ 2017] End-to-end learning for point cloud based 3d object detection

[Arxiv 2017] Frustum pointnets for 3d object detection from RGB-D data

2018

[CVPR 2018] FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation

[CVPR 2018] PPFNet: Global Context Aware Local Features for Robust 3D Point Matching

[CVPR 2018] PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation

[CVPR 2018] VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

[CVPR 2018] GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition

[CVPR 2018] Frustum PointNets for 3D Object Detection from RGB-D Data

[CVPR 2018] Learning 3D Shape Completion From Laser Scan Data With Weak Supervision

[CVPR 2018] End-to-end learning of key point detector and descriptor for pose invariant 3D matching

[CVPR 2018] Multi-Level Fusion based 3D Object Detection from Monocular Images

[CVPR 2018] SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation

[CVPR 2018] Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net

[CVPR 2018] Attentional ShapeContextNet for Point Cloud Recognition

[CVPR 2018] Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling

[CVPR 2018] PIXOR: Real-time 3D Object Detection from Point Clouds

[ECCV 2018] 3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation

[ECCV 2018] Fully-Convolutional Point Networks for Large-Scale Point Clouds

[NIPS 2018] PointCNN: Convolution On X -Transformed Points

[3DV 2018] 3DTNet: Learning Local Features Using 2D and 3D Cues

[3DV 2018] Efficient Convolutions for Real-Time Semantic Segmentation of 3D Point Clouds

[ArXiv 2018] Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation

[ArXiv 2018] RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement

[ArXiv 2018] PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation

[ArXiv 2018] Complex-YOLO: An Euler-Region-Proposal for Real-time 3D Object Detection on Point Clouds

[ArXiv 2018] SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

[sensors 2018] Dense RGB-D Semantic Mapping with Pixel-Voxel Neural Network

[IV 2018] Object Modeling from 3D Point Cloud Data for Self-Driving Vehicles

2019

[CVPR 2019] PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud

[CVPR 2019] PointPillars: Fast Encoders for Object Detection from Point Clouds