20 citations · 50 across the 27 of their papers we have counts for
17 papers · 1 filter
Geometry-Conditioned Visual Place Recognition in Natural Environments
Walter Nedov, Saimunur Rahman, Kavindie Katuwandeniya +3
Visual Place Recognition (VPR) in natural environments remains challenging due to repetitive vegetation, sparse distinctive landmarks, and substantial appearance and viewpoint vari…
TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation
Sutharsan Mahendran, Darshana Priyasad, Kaushik Roy +4
Cross-modal distillation from Vision Foundation Models (VFMs) to LiDAR backbones has recently emerged as a self-supervised pretraining strategy that reduces reliance on dense point…
Spatially Stratified Distillation for Heterogeneous Radar Place Recognition
Sagun Singh Shrestha, Samuel Harding, Abdelwahed Khamis +2
Scalable, all-weather place recognition increasingly relies on heterogeneous radar place recognition to bridge diverse hardware platforms. A notable application is matching queries…
Visual Place Recognition in Forests with Depth-Aware Distillation
Walter Nedov, Saimunur Rahman, Kavindie Katuwandeniya +3
Visual place recognition in natural forest environments remains challenging due to repetitive vegetation, weak structural cues, and significant appearance variation across traversa…
Cross-Modal Benchmarking for Robotic Perception in Natural Environments
David Hall, Joshua Knights, Mark Cox +1
Natural environments present a complex challenge to robotics perception systems. Current models, particularly vision foundation models, are largely trained on structured, urban env…
Ilov3Splat: Instance-Level Open-Vocabulary 3D Scene Understanding in Gaussian Splatting
Binh Long Nguyen, Kien Nguyen, Sridha Sridharan +2
We introduce Ilov3Splat, a novel framework for instance-level open-vocabulary 3D scene understanding built on 3D Gaussian Splatting (3D-GS). Most prior work depends on 2D rendering…
WildCross: A Cross-Modal Large Scale Benchmark for Place Recognition and Metric Depth Estimation in Natural Environments
Joshua Knights, Joseph Reid, Kaushik Roy +3
Recent years have seen a significant increase in demand for robotic solutions in unstructured natural environments, alongside growing interest in bridging 2D and 3D scene understan…
Dual-Domain Masked Image Modeling: A Self-Supervised Pretraining Strategy Using Spatial and Frequency Domain Masking for Hyperspectral Data
Shaheer Mohamed, Tharindu Fernando, Sridha Sridharan +2
Hyperspectral images (HSIs) capture rich spectral signatures that reveal vital material properties, offering broad applicability across various domains. However, the scarcity of la…
Spectral-Enhanced Transformers: Leveraging Large-Scale Pretrained Models for Hyperspectral Object Tracking
Shaheer Mohamed, Tharindu Fernando, Sridha Sridharan +2
Hyperspectral object tracking using snapshot mosaic cameras is emerging as it provides enhanced spectral information alongside spatial data, contributing to a more comprehensive un…
A Deeper Look into Second-Order Feature Aggregation for LiDAR Place Recognition
Saimunur Rahman, Peyman Moghadam
Efficient LiDAR Place Recognition (LPR) compresses dense pointwise features into compact global descriptors. While first-order aggregators such as GeM and NetVLAD are widely used,…
Point-PNG: Conditional Pseudo-Negatives Generation for Point Cloud Pre-Training
Sutharsan Mahendren, Saimunur Rahman, Piotr Koniusz +4
We propose Point-PNG, a novel self-supervised learning framework that generates conditional pseudo-negatives in the latent space to learn point cloud representations that are both…
SOLVR: Submap Oriented LiDAR-Visual Re-Localisation
Joshua Knights, Sebastián Barbas Laina, Peyman Moghadam +1
This paper proposes SOLVR, a unified pipeline for learning based LiDAR-Visual re-localisation which performs place recognition and 6-DoF registration across sensor modalities. We p…
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…