Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector
Explore this paper's citation graph
- Type
- preprint
- Published
- 2019-01-29
- Cited by
- 102
- References
- 44
- Access
- Open access
- OpenAlex
- https://openalex.org/W2918836500
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:59413927
Keywords
Lidar, Detector, Computer science, Artificial intelligence, Object (grammar)
References
- Support Vector Machine Active Learning with Applications to Text Classification
- The mathematical theory of communication
- Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
- Active Learning with Gaussian Processes for Object Categorization
- Are we ready for autonomous driving? The KITTI vision benchmark suite
- The Cityscapes Dataset for Semantic Urban Scene Understanding
- 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
- 3D fully convolutional network for vehicle detection in point cloud
- 3D Bounding Box Estimation Using Deep Learning and Geometry
- MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
- Fast LIDAR-based road detection using fully convolutional neural networks
- Deep Bayesian Active Learning with Image Data
- On Calibration of Modern Neural Networks
- A Tactile-Based Framework for Active Object Learning and Discrimination using Multimodal Robotic Skin
- Dropout Sampling for Robust Object Detection in Open-Set Conditions
- Cost-Effective Active Learning for Melanoma Segmentation
- Frustum PointNets for 3D Object Detection from RGB-D Data
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Cited by
- Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving
- Autolabeling 3D Objects With Differentiable Rendering of SDF Shape Priors
- Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving
- Noise-tolerant single photon sensitive three-dimensional imager
- Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
- Adaptive LiDAR Sampling and Depth Completion Using Ensemble Variance
- HYBRID ACQUISITION OF HIGH QUALITY TRAINING DATA FOR SEMANTIC SEGMENTATION OF 3D POINT CLOUDS USING CROWD-BASED ACTIVE LEARNING
- EFFICIENT TRAINING OF SEMANTIC POINT CLOUD SEGMENTATION VIA ACTIVE LEARNING
- Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty
- A Survey of Deep Active Learning
- Active and incremental learning for semantic ALS point cloud segmentation
- FLAVA: Find, Localize, Adjust and Verify to Annotate LiDAR-based Point Clouds
- A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving
- Embodied Visual Active Learning for Semantic Segmentation
- A Survey on Sensor Technologies for Unmanned Ground Vehicles
- Advanced Active Learning Strategies for Object Detection
- Learning Common and Transferable Feature Representations for Multi-Modal Data
- Review on Vehicle Detection Technology for Unmanned Ground Vehicles
- Offboard 3D Object Detection from Point Cloud Sequences
- Active Learning for Deep Object Detection via Probabilistic Modeling
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