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Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone

https://doi.org/10.1109/access.2021.3055491

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01270-0_39openalex-snapshot

Deep Continuous Fusion for Multi-sensor 3D Object Detection

inferredcites →https://doi.org/10.1007/978-3-030-26969-2_56openalex-snapshot

Fully Convolutional Neural Networks for 3D Vehicle Detection Based on Point Clouds

inferredcites →https://doi.org/10.1007/978-3-030-58583-9_43openalex-snapshot

3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-view Spatial Feature Fusion for 3D Object Detection

inferredcites →https://doi.org/10.1007/978-3-030-60796-8_12openalex-snapshot

LiDAR-Camera-Based Deep Dense Fusion for Robust 3D Object Detection

inferredcites →https://doi.org/10.1007/978-3-319-46493-0_38openalex-snapshot

Identity Mappings in Deep Residual Networks

inferredcites →https://doi.org/10.1016/j.isprsjprs.2020.06.016openalex-snapshot

Vehicle detection of multi-source remote sensing data using active fine-tuning network

inferredcites →https://doi.org/10.1109/access.2019.2927012openalex-snapshot

MCF3D: Multi-Stage Complementary Fusion for Multi-Sensor 3D Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2012.6248074openalex-snapshot

Are we ready for autonomous driving? The KITTI vision benchmark suite

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.16openalex-snapshot

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

inferredcites →https://doi.org/10.1109/cvpr.2017.691openalex-snapshot

Multi-view 3D Object Detection Network for Autonomous Driving

inferredcites →https://doi.org/10.1109/cvpr.2018.00033openalex-snapshot

PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation

inferredcites →https://doi.org/10.1109/cvpr.2018.00102openalex-snapshot

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

inferredcites →https://doi.org/10.1109/cvpr.2018.00472openalex-snapshot

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2019.00086openalex-snapshot

PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud

inferredcites →https://doi.org/10.1109/cvpr.2019.00752openalex-snapshot

Multi-Task Multi-Sensor Fusion for 3D Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2019.01298openalex-snapshot

PointPillars: Fast Encoders for Object Detection From Point Clouds

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00178openalex-snapshot

Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud

inferredcites →https://doi.org/10.1109/cvpr42600.2020.01054openalex-snapshot

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

inferredcites →https://doi.org/10.1109/cvpr42600.2020.01105openalex-snapshot

3DSSD: Point-Based 3D Single Stage Object Detector

inferredcites →https://doi.org/10.1109/cvpr42600.2020.01189openalex-snapshot

Structure Aware Single-Stage 3D Object Detection From Point Cloud

inferredcites →https://doi.org/10.1109/iccp.2017.8117023openalex-snapshot

Real-time object detection using a sparse 4-layer LIDAR

inferredcites →https://doi.org/10.1109/iccv.2019.00987openalex-snapshot

Fast Point R-CNN

inferredcites →https://doi.org/10.1109/icra.2018.8461232openalex-snapshot

A General Pipeline for 3D Detection of Vehicles

inferredcites →https://doi.org/10.1109/icra.2019.8794195openalex-snapshot

MVX-Net: Multimodal VoxelNet for 3D Object Detection

inferredcites →https://doi.org/10.1109/iros.2018.8594049openalex-snapshot

Joint 3D Proposal Generation and Object Detection from View Aggregation

inferredcites →https://doi.org/10.1109/ivs.2017.7995699openalex-snapshot

Velocity and shape from tightly-coupled LiDAR and camera

inferredcites →https://doi.org/10.1109/lgrs.2019.2919755openalex-snapshot

Fourier-Based Rotation-Invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection

inferredcites →https://doi.org/10.1109/tgrs.2020.3015157openalex-snapshot

Graph Convolutional Networks for Hyperspectral Image Classification

inferredcites →https://doi.org/10.1109/tgrs.2020.3016820openalex-snapshot

More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification

inferredcites →https://doi.org/10.1109/tii.2020.3048719openalex-snapshot

Three-Attention Mechanisms for One-Stage 3-D Object Detection Based on LiDAR and Camera

inferredcites →https://doi.org/10.1109/tpami.2018.2858826openalex-snapshot

Focal Loss for Dense Object Detection

inferredcites →https://doi.org/10.23919/iccas50221.2020.9268367openalex-snapshot

3D SaccadeNet: A Single-Shot 3D Object Detector for LiDAR Point Clouds

inferredcites →https://doi.org/10.3390/rs12111895openalex-snapshot

KDA3D: Key-Point Densification and Multi-Attention Guidance for 3D Object Detection

inferredcites →https://doi.org/10.3390/s18103337openalex-snapshot

SECOND: Sparsely Embedded Convolutional Detection

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Object Detection in Autonomous Cyber-Physical Vehicle Platforms: Status and Open Challenges

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Apprentissage continu en ligne pour la détection 3D des usagers de la route en conduite autonome

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