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Found 14 papers, 8 papers with code
Exploring Semi-Supervised Learning for Online Mapping
The performance gap to using all labels is reduced from 29. 6 to 3. 4 mIoU on Argoverse, and from 12 to 3. 4 mIoU on NuScenes utilising only 10% of the labelled data.
ProSub: Probabilistic Open-Set Semi-Supervised Learning with Subspace-Based Out-of-Distribution Detection
Moreover, we propose an approach to estimate the conditional distributions of scores given ID or OOD data, enabling probabilistic predictions of data being ID or OOD.
Are NeRFs ready for autonomous driving? Towards closing the real-to-simulation gap
Specifically, we evaluate object detectors and an online mapping model on real and simulated data, and study the effects of different fine-tuning strategies. Our results show notable improvements in model robustness to simulated data, even improving real-world performance in some cases.
Localization Is All You Evaluate: Data Leakage in Online Mapping Datasets and How to Fix It
Specifically, over $80$% of nuScenes and $40$% of Argoverse 2 validation and test samples are less than $5$ m from a training sample.
Improving Open-Set Semi-Supervised Learning with Self-Supervision
Open-set semi-supervised learning (OSSL) embodies a practical scenario within semi-supervised learning, wherein the unlabeled training set encompasses classes absent from the labeled set.
DoubleMatch: Improving Semi-Supervised Learning with Self-Supervision
Following the success of supervised learning, semi-supervised learning (SSL) is now becoming increasingly popular.
Extended Object Tracking Using Sets Of Trajectories with a PHD Filter
PHD filtering is a common and effective multiple object tracking (MOT) algorithm used in scenarios where the number of objects and their states are unknown.
Back to the Feature: Learning Robust Camera Localization from Pixels to Pose
In this paper, we go Back to the Feature: we argue that deep networks should focus on learning robust and invariant visual features, while the geometric estimation should be left to principled algorithms.
Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization
In this paper, we propose a new neural network, the Fine-Grained Segmentation Network (FGSN), that can be used to provide image segmentations with a larger number of labels and can be trained in a self-supervised fashion.
A Cross-Season Correspondence Dataset for Robust Semantic Segmentation
We show that adding the correspondences as extra supervision during training improves the segmentation performance of the convolutional neural network, making it more robust to seasonal changes and weather conditions.
Poisson Multi-Bernoulli Mapping Using Gibbs Sampling
The proposed method can handle uncertainties in the data associations and the cardinality of the set of landmarks, and is parallelizable, making it suitable for large-scale problems.
Semantic Match Consistency for Long-Term Visual Localization
Robust and accurate visual localization across large appearance variations due to changes in time of day, seasons, or changes of the environment is a challenging problem which is of importance to application areas such as navigation of autonomous robots.
Long-term Visual Localization using Semantically Segmented Images
Robust cross-seasonal localization is one of the major challenges in long-term visual navigation of autonomous vehicles.
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds.
