20 citations · 50 across the 27 of their papers we have counts for
6 papers · 1 filter
WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environments
Kavisha Vidanapathirana, Joshua Knights, Stephen Hausler +8
Recent progress in semantic scene understanding has primarily been enabled by the availability of semantically annotated bi-modal (camera and LiDAR) datasets in urban environments.…
TULIP: Transformer for Upsampling of LiDAR Point Clouds
Bin Yang, Patrick Pfreundschuh, Roland Siegwart +3
LiDAR Upsampling is a challenging task for the perception systems of robots and autonomous vehicles, due to the sparse and irregular structure of large-scale scene contexts. Recent…
Pre-training with Random Orthogonal Projection Image Modeling
Maryam Haghighat, Peyman Moghadam, Shaheer Mohamed +1
Masked Image Modeling (MIM) is a powerful self-supervised strategy for visual pre-training without the use of labels. MIM applies random crops to input images, processes them with…
Multivariate Prototype Representation for Domain-Generalized Incremental Learning
Can Peng, Piotr Koniusz, Kaiyu Guo +2
Deep learning models suffer from catastrophic forgetting when being fine-tuned with samples of new classes. This issue becomes even more pronounced when faced with the domain shift…
FactoFormer: Factorized Hyperspectral Transformers with Self-Supervised Pretraining
Shaheer Mohamed, Maryam Haghighat, Tharindu Fernando +3
Hyperspectral images (HSIs) contain rich spectral and spatial information. Motivated by the success of transformers in the field of natural language processing and computer vision…
Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition
Milad Ramezani, Liang Wang, Joshua Knights +3
This paper proposes a pose-graph attentional graph neural network, called P-GAT, which compares (key)nodes between sequential and non-sequential sub-graphs for place recognition ta…