Frustum PointNets for 3D Object Detection from RGB-D Data
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- Type
- preprint
- Published
- 2017-11-22
- Cited by
- 2,562
- References
- 44
- Access
- Open access
- OpenAlex
- https://openalex.org/W2769205412
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:4868248
Keywords
Point cloud, Artificial intelligence, Computer vision, Computer science, RGB color model
References
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Multi-view Convolutional Neural Networks for 3D Shape Recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- SUN RGB-D: A RGB-D scene understanding benchmark suite
- Data-driven 3D Voxel Patterns for object category recognition
- Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
- Vision meets robotics: The KITTI dataset
- Are we ready for autonomous driving? The KITTI vision benchmark suite
- 3D Object Proposals for Accurate Object Class Detection
- VoxNet: A 3D Convolutional Neural Network for real-time object recognition
- Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- Voting for Voting in Online Point Cloud Object Detection
- Volumetric and Multi-view CNNs for Object Classification on 3D Data
- Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients
- Monocular 3D Object Detection for Autonomous Driving
- Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks
- Multi-view 3D Object Detection Network for Autonomous Driving
- OctNet: Learning Deep 3D Representations at High Resolutions
- 3D fully convolutional network for vehicle detection in point cloud
Cited by
- Indoor Scene Understanding in 2.5/3D: A Survey
- Complex-YOLO: Real-time 3D Object Detection on Point Clouds
- Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection
- BirdNet: A 3D Object Detection Framework from LiDAR Information
- LMNet: Real-time Multiclass Object Detection on CPU Using 3D LiDAR
- PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation
- Leveraging Pre-Trained 3D Object Detection Models for Fast Ground Truth Generation
- Parsing Geometry Using Structure-Aware Shape Templates
- Focal Loss in 3D Object Detection
- Generating 3D Adversarial Point Clouds
- Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks
- Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection
- One-shot learning for RGB-D hand-held object recognition
- Deep Continuous Fusion for Multi-sensor 3D Object Detection
- Multi-view X-ray R-CNN
- A 3D Dynamic Scene Analysis Framework for Development of Intelligent Transportation Systems
- Point Cloud Object Recognition using 3D Convolutional Neural Networks
- SECOND: Sparsely Embedded Convolutional Detection
- HDNET: Exploiting HD Maps for 3D Object Detection
- RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement
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