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Found 16 papers, 2 papers with code
S3PT: Scene Semantics and Structure Guided Clustering to Boost Self-Supervised Pre-Training for Autonomous Driving
Recent self-supervised clustering-based pre-training techniques like DINO and Cribo have shown impressive results for downstream detection and segmentation tasks.
LetsMap: Unsupervised Representation Learning for Semantic BEV Mapping
Our approach pretrains the network to independently reason about scene geometry and scene semantics using two disjoint neural pathways in an unsupervised manner and then finetunes it for the task of semantic BEV mapping using only a small fraction of labels in the BEV.
BEVCar: Camera-Radar Fusion for BEV Map and Object Segmentation
Semantic scene segmentation from a bird's-eye-view (BEV) perspective plays a crucial role in facilitating planning and decision-making for mobile robots.
Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset
Active Learning (AL) has remained relatively unexplored for LiDAR perception tasks in autonomous driving datasets.
Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles
Autonomous driving (AD) perception today relies heavily on deep learning based architectures requiring large scale annotated datasets with their associated costs for curation and annotation.
Self-Supervised 3D Monocular Object Detection by Recycling Bounding Boxes
We evaluate the pretext task using the RTM3D detection model as baseline, with and without the application of data augmentation.
Simulation-to-Reality domain adaptation for offline 3D object annotation on pointclouds with correlation alignment
Annotating objects with 3D bounding boxes in LiDAR pointclouds is a costly human driven process in an autonomous driving perception system.
LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation
We observe that data augmentation achieves full dataset accuracy using only 60\% of samples from the selected dataset configuration.
Exploring 2D Data Augmentation for 3D Monocular Object Detection
Extension of these data augmentations for 3D object detection requires adaptation of the 3D geometry of the input scene and synthesis of new viewpoints.
Road Segmentation on low resolution Lidar point clouds for autonomous vehicles
In our experiments the low resolution 16/32 layer LIDAR point clouds are simulated by subsampling the original 64 layer data, for subsequent transformation in to a feature map in the Bird-Eye-View (BEV) and SphericalView (SV) representations of the point cloud.
Deep Reinforcement Learning for Autonomous Driving: A Survey
With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments.
Exploring applications of deep reinforcement learning for real-world autonomous driving systems
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo.
Regression and Classification by Zonal Kriging
Consider a family $Z=\{\boldsymbol{x_{i}}, y_{i}$,$1\leq i\leq N\}$ of $N$ pairs of vectors $\boldsymbol{x_{i}} \in \mathbb{R}^d$ and scalars $y_{i}$ that we aim to predict for a new sample vector $\mathbf{x}_0$.
Real-time Dynamic Object Detection for Autonomous Driving using Prior 3D-Maps
In this paper, we discuss real-time dynamic object detection algorithms which leverages previously mapped Lidar point clouds to reduce processing.
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos
Videos represent the primary source of information for surveillance applications and are available in large amounts but in most cases contain little or no annotation for supervised learning.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
+1
Rejection-Cascade of Gaussians: Real-time adaptive background subtraction framework
We achieve a good improvement in speed without compromising the accuracy with respect to the baseline GMM model.
