VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
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- Type
- preprint
- Published
- 2017-11-17
- Cited by
- 4,679
- References
- 46
- Access
- Open access
- OpenAlex
- https://openalex.org/W2769571673
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:42427078
Keywords
Point cloud, Computer science, Artificial intelligence, Feature (linguistics), Computer vision
References
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- 3D Convolutional Neural Networks for landing zone detection from LiDAR
- Multiview random forest of local experts combining RGB and LIDAR data for pedestrian detection
- Point Signatures: A New Representation for 3D Object Recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Some Improvements on Deep Convolutional Neural Network Based Image Classification
- Joint SFM and detection cues for monocular 3D localization in road scenes
- Data-driven 3D Voxel Patterns for object category recognition
- Pedestrian detection combining RGB and dense LIDAR data
- Structural Indexing: Efficient 3-D Object Recognition
- Voting-based pose estimation for robotic assembly using a 3D sensor
- Real-time human pose recognition in parts from single depth images
- Are Cars Just 3D Boxes? Jointly Estimating the 3D Shape of Multiple Objects
- Depth kernel descriptors for object recognition
- Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
- Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
- Detailed 3D Representations for Object Recognition and Modeling
- Multiple 3D Object tracking for augmented reality
- Scale-hierarchical 3D object recognition in cluttered scenes
- On the Repeatability and Quality of Keypoints for Local Feature-based 3D Object Retrieval from Cluttered Scenes
Cited by
- Complex-YOLO: Real-time 3D Object Detection on Point Clouds
- Detection, localisation and tracking of pallets using machine learning techniques and 2D range data
- Deep Person Detection in 2D Range Data
- 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
- Automated Segmentation of Epithelial Tissue Using Cycle-Consistent Generative Adversarial Networks
- Noisy lidar point clouds: impact on information extraction in high-precision lidar surveying
- LMNet: Real-time Multiclass Object Detection on CPU Using 3D LiDAR
- Deep Person Detection in Two-Dimensional Range Data
- PointFlowNet: Learning Representations for 3D Scene Flow Estimation from Point Clouds
- OpenVDAP: An Open Vehicular Data Analytics Platform for CAVs
- Machine Learning Assisted High-Definition Map Creation
- RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving
- PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud
- ChipNet: Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA
- Satellite selection with an end-to-end deep learning network
- Focal Loss in 3D Object Detection
- PointNetGPD: Detecting Grasp Configurations from Point Sets
- Generating 3D Adversarial Point Clouds
- Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach
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