Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection
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- Type
- preprint
- Published
- 2018-09-14
- Cited by
- 75
- References
- 44
- Access
- Open access
- OpenAlex
- https://openalex.org/W2891685396
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:52288693
Keywords
Benchmark (surveying), Computer science, Lidar, Inference, Heteroscedasticity
References
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Keeping the neural networks simple by minimizing the description length of the weights
- Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
- Practical Variational Inference for Neural Networks
- A Practical Bayesian Framework for Backpropagation Networks
- Are we ready for autonomous driving? The KITTI vision benchmark suite
- Modelling uncertainty in deep learning for camera relocalization
- Voting for Voting in Online Point Cloud Object Detection
- Fusing LIDAR and images for pedestrian detection using convolutional neural networks
- Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks
- 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
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- Robust detection of non-motorized road users using deep learning on optical and LIDAR data
- Fast LIDAR-based road detection using fully convolutional neural networks
- Deep Bayesian Active Learning with Image Data
- Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
- Combining LiDAR space clustering and convolutional neural networks for pedestrian detection
- Vehicle detection and localization on bird's eye view elevation images using convolutional neural network
Cited by
- Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems
- Capturing Object Detection Uncertainty in Multi-Layer Grid Maps
- BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving
- Bounding Box Regression With Uncertainty for Accurate Object Detection
- Robust Aleatoric Modeling for Future Vehicle Localization
- Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector
- Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving
- DCTD: Deep Conditional Target Densities for Accurate Regression
- Learning an Uncertainty-Aware Object Detector for Autonomous Driving
- Brake Maneuver Prediction – An Inference Leveraging RNN Focus on Sensor Confidence
- Learning Deep Conditional Target Densities for Accurate Regression
- Fusion of 3D LIDAR and Camera Data for Object Detection in Autonomous Vehicle Applications
- Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving
- SYMOG: learning symmetric mixture of Gaussian modes for improved fixed-point quantization
- Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
- Inferring Spatial Uncertainty in Object Detection
- Uncertainty depth estimation with gated images for 3D reconstruction
- Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles
- A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications
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