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RoIFusion: 3D Object Detection From LiDAR and Vision

https://doi.org/10.1109/access.2021.3070379

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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/10.1007/978-3-030-11009-3_11openalex-snapshot

Complex-YOLO: An Euler-Region-Proposal for Real-Time 3D Object Detection on Point Clouds

inferredcites →https://doi.org/10.1007/978-3-030-32248-9_23openalex-snapshot

VoteNet: A Deep Learning Label Fusion Method for Multi-atlas Segmentation

inferredcites →https://doi.org/10.1016/j.isprsjprs.2018.10.006openalex-snapshot

Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification

inferredcites →https://doi.org/10.1016/j.isprsjprs.2020.06.014openalex-snapshot

X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data

inferredcites →https://doi.org/10.1109/cisp-bmei48845.2019.8965844openalex-snapshot

FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds

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.2016.91openalex-snapshot

You Only Look Once: Unified, Real-Time Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2017.106openalex-snapshot

Feature Pyramid Networks for Object Detection

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.2018.00798openalex-snapshot

PIXOR: Real-time 3D Object Detection from Point Clouds

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.2019.00783openalex-snapshot

Stereo R-CNN Based 3D Object Detection for Autonomous Driving

inferredcites →https://doi.org/10.1109/cvpr.2019.00864openalex-snapshot

Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

inferredcites →https://doi.org/10.1109/cvpr.2019.00910openalex-snapshot

Relation-Shape Convolutional Neural Network for Point Cloud Analysis

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/cvpr42600.2020.00466openalex-snapshot

PointPainting: Sequential Fusion for 3D Object Detection

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/iccv.2017.324openalex-snapshot

Focal Loss for Dense Object Detection

inferredcites →https://doi.org/10.1109/iccv.2019.00204openalex-snapshot

STD: Sparse-to-Dense 3D Object Detector for Point Cloud

inferredcites →https://doi.org/10.1109/iccv.2019.00667openalex-snapshot

CenterNet: Keypoint Triplets for Object Detection

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

Fast Point R-CNN

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/iros40897.2019.8968513openalex-snapshot

Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection

inferredcites →https://doi.org/10.1109/itsc.2018.8569311openalex-snapshot

BirdNet: A 3D Object Detection Framework from LiDAR Information

inferredcites →https://doi.org/10.1109/tcyb.2018.2856821openalex-snapshot

DIOD: Fast and Efficient Weakly Semi-Supervised Deep Complex ISAR Object Detection

inferredcites →https://doi.org/10.1109/tcyb.2019.2933224openalex-snapshot

Real-World ISAR Object Recognition Using Deep Multimodal Relation Learning

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/tpami.2016.2577031openalex-snapshot

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

inferredcites →https://doi.org/10.1109/tpami.2020.2977026openalex-snapshot

From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network

inferredcites →https://doi.org/10.1145/3065386openalex-snapshot

ImageNet classification with deep convolutional neural networks

inferredcites →https://doi.org/10.1145/3326362openalex-snapshot

Dynamic Graph CNN for Learning on Point Clouds

inferredcites →https://doi.org/10.1177/0278364913491297openalex-snapshot

Vision meets robotics: The KITTI dataset

inferredcites →https://doi.org/10.1609/aaai.v34i07.6837openalex-snapshot

TANet: Robust 3D Object Detection from Point Clouds with Triple Attention

inferredcites →https://doi.org/10.3390/app10072391openalex-snapshot

Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions

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

SECOND: Sparsely Embedded Convolutional Detection

inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot

Adam: A Method for Stochastic Optimization

inferredcites →https://doi.org/10.48550/arxiv.1706.01307openalex-snapshot

Submanifold Sparse Convolutional Networks

inferredcites →https://doi.org/10.48550/arxiv.1706.05587openalex-snapshot

Rethinking Atrous Convolution for Semantic Image Segmentation

inferredcites →https://doi.org/10.48550/arxiv.1812.05276openalex-snapshot

IPOD: Intensive Point-based Object Detector for Point Cloud

inferredcites →https://doi.org/10.48550/arxiv.1905.08705openalex-snapshot

GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloud

inferredcites →https://doi.org/10.48550/arxiv.1910.04093openalex-snapshot

Patch Refinement -- Localized 3D Object Detection

What points at this work

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

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A Comprehensive Review: 3d Object Detection Based on Visible Light Camera, Infrared Camera, and Lidar in Dark Scene

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Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2022

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Living Earth—A Methodology for Modeling the Environment of Construction Sites Via Digital Twins

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Choosing Computer Vision Based Technologies for Traffic Safety Assessment at Intersections

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Study of the Properties and Application of LIDAR Technology for Detection Unmanned Aerial Vehicles (UAVs)

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Machine Learning for Sensor Data Processing in Autonomous Robotic and Drone Systems

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A Review of Decision-Making and Planning Technologies for Unmanned Driving in Mining Environments: Current Status, Applicability, and Future Directions

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Deep learning-based fusion networks with high-order attention mechanism for 3D object detection in autonomous driving scenarios

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Multimodal and view image processing for three-dimensional object detection in autonomous driving

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Dense frustum-aware fusion for 3D object detection in perception systems

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Efficient graph attentional network for 3D object detection from Frustum-based LiDAR point clouds

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MAAFOcc: Multimodal adaptive asymmetric fusion based occupancy prediction

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Infrastructure-assisted 3D detection networks based on camera-lidar early fusion strategy

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Scene captioning with deep fusion of images and point clouds

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Temporal dynamics and multimodal data fusion for robust 3D object detection in IoV systems

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A LiDAR - camera fusion detection method based on weight allocation

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Spectral Pyramid Pooling and Fused Keypoint Generation in ResNet-50 for Robust 3D Object Detection

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VKP-P3D: Real-Time Monocular Pseudo 3D Object Detection Based on Visible Key Points and Camera Geometry

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ContextNet: Leveraging Comprehensive Contextual Information for Enhanced 3D Object Detection

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ESFormer: A Pillar-Based Object Detection Method Based on Point Cloud Expansion Sampling and Optimised Swin Transformer

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Implementation of Object Detection for Autonomous Vehicles by LiDAR and Camera Fusion

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SFEF: Transformer-based LiDAR-Camera Object Detection for Autonomous Driving

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3D intelligent fusion algorithm with data reconstruction ability

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Advancements and Applications of LiDAR Technology in the Modern World: A Comprehensive Review

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A Framework for Visualization of Streaming 3D Point Cloud

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Improving Interaction-Based Vehicle Trajectory Prediction via Handling Sensing Failures

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Toward a Robust Sensor Fusion Step for 3D Object Detection on Corrupted Data

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Deep Learning-Based Approach for Efficient 3D Object Detection in Autonomous Vehicles

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Foreground-Aware Geometry Compression With Hybrid Attention for Large-Scale Point Clouds

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Large Data Transfer Optimization for Improved Robustness in Real-Time V2X-Communication

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Survey on Image and Point-Cloud Fusion-Based Object Detection in Autonomous Vehicles

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Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and Outlook

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Scalable Fail-Degraded Systems for Autonomous Vehicles: A Survey

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Performance and Challenges of 3D Object Detection Methods in Complex Scenes for Autonomous Driving

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Multi-Modal 3D Object Detection in Autonomous Driving: A Survey and Taxonomy

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