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Found 8 papers, 0 papers with code
3D Adversarial Augmentations for Robust Out-of-Domain Predictions
We conduct extensive experiments across a variety of scenarios on data from KITTI, Waymo, and CrashD for 3D object detection, and on data from SemanticKITTI, Waymo, and nuScenes for 3D semantic segmentation.
Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning
We propose a novel semi-supervised active learning (SSAL) framework for monocular 3D object detection with LiDAR guidance (MonoLiG), which leverages all modalities of collected data during model development.
Active Learning for Object Detection with Non-Redundant Informative Sampling
Curating an informative and representative dataset is essential for enhancing the performance of 2D object detectors.
Multi-Task Consistency for Active Learning
In single-task vision-based settings, inconsistency-based active learning has proven to be effective in selecting informative samples for annotation.
Segmenting Known Objects and Unseen Unknowns without Prior Knowledge
By doing so, for the first time in panoptic segmentation with unknown objects, our U3HS is trained without unknown categories, reducing assumptions and leaving the settings as unconstrained as in real-life scenarios.
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection
Despite training only on a standard dataset, such as KITTI, augmenting with our vector fields significantly improves the generalization to differently shaped objects and scenes.
CertainNet: Sampling-free Uncertainty Estimation for Object Detection
Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings.
Panoster: End-to-end Panoptic Segmentation of LiDAR Point Clouds
Panoptic segmentation has recently unified semantic and instance segmentation, previously addressed separately, thus taking a step further towards creating more comprehensive and efficient perception systems.
